Use of SAR and Optical Time Series for Tropical Forest Disturbance Mapping

Hirschmugl, Manuela; Deutscher, Janik; Sobe, Carina; Bouvet, Alexandre; Mermoz, Stéphane; Schardt, Mathias

doi:10.3390/rs12040727

Cited by 61 publications

(46 citation statements)

References 76 publications

(131 reference statements)

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“…For example, Reiche et al [42] used the Sentinel-1 radar and optical MODIS data to determine tropical forest cover loss in Indonesia, and achieved producer's and user's accuracy of 95%. Radar data has also been used for tropical forest disturbance mapping in Peru and Gabon [43]. The authors combined a time series of optical Landsat-8, Sentinel-2 with radar Sentinel-1, and mapped non-forest, undisturbed forest and disturbed forest, obtaining an overall accuracy of 93% for Peru and 98% for Gabon.…”

Section: Discussionmentioning

confidence: 99%

Assessment of Sentinel-2 Satellite Images and Random Forest Classifier for Rainforest Mapping in Gabon

Waśniewski

Hościło

Zagajewski

et al. 2020

Forests

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This study is focused on the assessment of the potential of Sentinel-2 satellite images and the Random Forest classifier for mapping forest cover and forest types in northwest Gabon. The main goal was to investigate the impact of various spectral bands collected by the Sentinel-2 satellite, normalized difference vegetation index (NDVI) and digital elevation model (DEM), and their combination on the accuracy of the classification of forest cover and forest type. Within the study area, five classes of forest type were delineated: semi-evergreen moist forest, lowland forest, freshwater swamp forest, mangroves, and disturbed natural forest. The classification was performed using the Random Forest (RF) classifier. The overall accuracy for the forest cover ranged between 92.6% and 98.5%, whereas for forest type, the accuracy was 83.4 to 97.4%. The highest accuracy for forest cover and forest type classifications were obtained using a combination of spectral bands at spatial resolutions of 10 m and 20 m and DEM. In both cases, the use of the NDVI did not increase the classification accuracy. The DEM was shown to be the most important variable in distinguishing the forest type. Among the Sentinel-2 spectral bands, the red-edge followed by the SWIR contributed the most to the accuracy of the forest type classification. Additionally, the Random Forest model for forest cover classification was successfully transferred from one master image to other images. In contrast, the transferability of the forest type model was more complex, because of the heterogeneity of the forest type and environmental conditions across the study area.

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Section: Discussionmentioning

confidence: 99%

Assessment of Sentinel-2 Satellite Images and Random Forest Classifier for Rainforest Mapping in Gabon

Waśniewski

Hościło

Zagajewski

et al. 2020

Forests

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“…These forest and land cover maps such as those produced by Hansen et al (2013) and European Space Agency (2015) are important for comparisons and sources of reference land cover maps. Radar-and opticalbased land cover maps are not very comparable and perhaps fusion methods are a way forward on this issue (Hirschmugl et al, 2020;Joshi et al, 2015).…”

Section: Satellite Image and Land Cover Applicationmentioning

confidence: 99%

Interpolating missing land cover data using stochastic spatial random forests for improved change detection

Holloway-Brown

Helmstedt

Mengersen

2021

Remote Sens Ecol Conserv

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Forest cover requires large scale and frequent monitoring as an indicator of biodiversity and progress towards United Nations and World Bank Sustainable Development Goal 15. Measuring change in forest cover over time is an essential task in order to track and preserve quality habitats for species around the world. Due to the prohibitive expense and impracticality of mass field data collection to monitor forest cover at regular intervals, satellite images are a key data source for monitoring forest cover globally. A challenge of working with satellite images is missing data due to clouds. Existing methods for interpolating the missing data based on past images, such as compositing, are effective for stable land cover but can be inaccurate for dynamic and substantially changing landscapes. Here we present an adaptation of our recent stochastic spatial random forest (SS-RF) method, which combines observed data from a prior image and modelled estimates of the current image to produce interpolated land cover values and associated probabilities of those values. Results show our SS-RF method accurately detected simulated land cover change under both clear felling (0.83 average overall accuracy) and tree thinning (0.85 average overall accuracy). Our method detected forest cover change substantially more accurately than compositing, offering 39% and 12% increases in average overall accuracy for clear felling and tree thinning simulations respectively. However, when natural fluctuation occurs and there is minimal change in land cover, compositing has equivalent or more accurate performance than our method. Overall we find that our SS-RF method produces accurate estimates under a range of simulated forest clearing scenarios and has a more accurate and robust performance than compositing when modelling noticeably changing landscapes.

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“…The sensitivities of both sensors towards forest disturbances vary. Optical sensors are capable of detecting subtle changes in the tree foliage but show limitations when separating those from higher magnitude changes such as tree removal, while radar sensors are capable of detecting larger structural changes in forest cover but are not able to identify changes in tree foliage that do not result in structural forest changes [36,[48][49][50]. However, their synergistic potential for a more detailed temporal characterization of fire-related forest disturbances has yet to be studied [36].…”

Section: Introductionmentioning

confidence: 99%

Exploring Archetypes of Tropical Fire-Related Forest Disturbances Based on Dense Optical and Radar Satellite Data and Active Fire Alerts

Balling

Verbesselt

Herold

et al. 2021

Forests

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Tropical forest disturbances linked to fire usage cause large amounts of greenhouse gas (GHG) emissions and environmental damages. Supporting precise GHG estimations and counteracting illegal fire usages in the tropics require timely and thematically detailed large-scale information on fire-related forest disturbances. Multi-sensor optical and radar detection and ranging (radar) remote sensing data combined with active fire alerts shows the potential for a more in-depth characterization of fire-related forest disturbances. We utilized dense optical (Landsat-7, Landsat-8 and Sentinel-2) and radar (Sentinel-1) time series to individually map forest disturbances in the province of Riau (Indonesia) for 2018–2019. We combined the sensor-specific optical and radar forest disturbance maps with daily active fire alerts and classified their temporal relationship (predating, coinciding, postdating) into seven so-called archetypes of fire-related forest disturbances. The archetypes reflect sensor-specific sensitives of optical (e.g., changes in tree foliage) and radar (e.g., changes in tree structure) data to detect varying types of forest disturbances, ranging from either a loss of tree foliage and/or structure predating, coinciding or postdating fires. These can be related to different magnitudes of fire-related forest disturbances and burn severities and can be associated with specific land management practices, such as slash-and-burn agriculture and salvage logging. This can support policy development, local and regional forest management and law enforcement to reduce illegal fire usage in the tropics. Results suggest that a delayed or opposing forest disturbance detection in the optical and radar signal is not only caused by environmental influences or different observation densities but, in some cases, such as fire-related forest disturbances, can be related to their different sensitives to detect changes in tree foliage and structure. Multi-sensor-based forest monitoring approaches should, therefore, not simply combine optical and radar time series on a data level, as it bears the risk of introducing artefacts.

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Use of SAR and Optical Time Series for Tropical Forest Disturbance Mapping

Cited by 61 publications

References 76 publications

Assessment of Sentinel-2 Satellite Images and Random Forest Classifier for Rainforest Mapping in Gabon

Assessment of Sentinel-2 Satellite Images and Random Forest Classifier for Rainforest Mapping in Gabon

Interpolating missing land cover data using stochastic spatial random forests for improved change detection

Exploring Archetypes of Tropical Fire-Related Forest Disturbances Based on Dense Optical and Radar Satellite Data and Active Fire Alerts

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