Wide-Area Near-Real-Time Monitoring of Tropical Forest Degradation and Deforestation Using Sentinel-1

Hoekman, D.H.; Kooij, Boris; Quiñónes, Marcela; Vellekoop, Sam; Carolita, Ita; Budhiman, Syarif; Arief, Rahmat; Roswintiarti, Orbita

doi:10.3390/rs12193263

Cited by 38 publications

(23 citation statements)

References 46 publications

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“…Other methods potentially suitable for mapping windthrown forests with SAR data include approaches demonstrated in other studies of natural and/or anthropogenic forest disturbance. These include mapping snow-damaged forest areas [23], monitoring selective logging and thinning operations in boreal and tropical forest biomes [11,[24][25][26][27][28], forest clear cutting and other forest changes [29][30][31][32][33][34].…”

Section: Windstorm Damage Studies With Sarmentioning

confidence: 99%

Detection of Forest Windstorm Damages with Multitemporal SAR Data—A Case Study: Finland

et al. 2021

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The purpose of this study was to develop methods to localize forest windstorm damages, assess their severity and estimate the total damaged area using space-borne SAR data. The development of the methods is the first step towards an operational system for near-real-time windstorm damage monitoring, with a latency of only a few days after the storm event in the best case. Windstorm detection using SAR data is not trivial, particularly at C-band. It can be expected that a large-area and severe windstorm damage may affect backscatter similar to clear cutting operation, that is, decrease the backscatter intensity, while a small area damage may increase the backscatter of the neighboring area, due to various scattering mechanisms. The remaining debris and temporal variation in the weather conditions and possible freeze–thaw transitions also affect observed backscatter changes. Three candidate windstorm detection methods were suggested, based on the improved k-nn method, multinomial logistic regression and support vector machine classification. The approaches use multitemporal ESA Sentinel-1 C-band SAR data and were evaluated in Southern Finland using wind damage data from the summer 2017, together with 27 Sentinel-1 scenes acquired in 2017 and other geo-referenced data. The stands correctly predicted severity category corresponded to 79% of the number of the stands in the validation data, and already 75% when only one Sentinel-1 scene after the damage was used. Thus, the damaged forests can potentially be localized with proposed tools within less than one week after the storm damage. In this study, the achieved latency was only two days. Our preliminary results also indicate that the damages can be localized even without separate training data.

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Section: Windstorm Damage Studies With Sarmentioning

confidence: 99%

Detection of Forest Windstorm Damages with Multitemporal SAR Data—A Case Study: Finland

et al. 2021

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“…Logging roads are visible in Figure 5 (11), whereas large areas of timber and particularly rubber plantations can be seen in Figure 5(9) and Figure 5(11)-( 13), as well as cashew plantations in Figure 5(10)-( 13) [32]. Although southwestern Cambodia has been partially preserved thus far, as in the Cardamom mountains that constitute one of the last remaining natural forests in southeast Asia, Figure 5 (10) shows that forest loss occurs in this region.…”

Section: Forest Loss Mapmentioning

confidence: 99%

“…occur in protected areas, emphasizing the lack of effectiveness of the protection and conservation of natural resources and biodiversity in protected areas. (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13). Forest areas from [20] are in dark green, and the background image is taken from Google Earth.…”

Section: Forest Loss Mapmentioning

confidence: 99%

“…However, large-scale forest loss mapping emerged very recently. The authors of [9,10] mapped forest loss over areas of approximately 265,000 km 2 and 227,000 km 2 , respectively. In [11], the authors used Google Earth Engine to develop a method for forest loss detection over the Brazilian Amazon, which may lead to the creation of an automated, cloud-based deforestation detection system running on INPE's servers.…”

Section: Introductionmentioning

confidence: 99%

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Continuous Detection of Forest Loss in Vietnam, Laos, and Cambodia Using Sentinel-1 Data

et al. 2021

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In this study, we demonstrate the ability of a new operational system to detect forest loss at a large scale accurately and in a timely manner. We produced forest loss maps every week over Vietnam, Cambodia, and Laos (>750,000 km2 in total) using Sentinel-1 data. To do so, we used the forest loss detection method based on shadow detection. The main advantage of this method is the ability to avoid false alarms, which is relevant in Southeast Asia where the areas of forest disturbance may be very small and scattered and detection is used for alert purposes. The estimated user accuracy of the forest loss map was 0.95 for forest disturbances and 0.99 for intact forest, and the estimated producer’s accuracy was 0.90 for forest disturbances and 0.99 for intact forest, with a minimum mapping unit of 0.1 ha. This represents an important step forward compared to the values achieved by previous studies. We also found that approximately half of forest disturbances in Cambodia from 2018 to 2020 occurred in protected areas, which emphasizes the lack of efficiency in the protection and conservation of natural resources in protected areas. On an annual basis, the forest loss areas detected using our method are found to be similar to the estimations from Global Forest Watch. These results highlight the fact that this method provides not only quick alerts but also reliable detections that can be used to calculate weekly, monthly, or annual forest loss statistics at a national scale.

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“…Optical forest disturbance maps are affected by less dense time series due to limited availability of cloud-free observations causing omission errors and spectral anomalies due to remaining atmospheric noise or cloud shadow causing commission errors [17,85]. In contrast, radar forest disturbance maps are affected by unnoticed small-scale disturbances in canopy gaps due to SAR's side-looking nature causing omission errors and fluctuating backscatter values due to changes in canopy and soil moisture causing commission errors [68,[86][87][88].…”

Section: Forest Disturbance Mapping and Fire Activitymentioning

confidence: 99%

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

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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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Wide-Area Near-Real-Time Monitoring of Tropical Forest Degradation and Deforestation Using Sentinel-1

Cited by 38 publications

References 46 publications

Detection of Forest Windstorm Damages with Multitemporal SAR Data—A Case Study: Finland

Detection of Forest Windstorm Damages with Multitemporal SAR Data—A Case Study: Finland

Continuous Detection of Forest Loss in Vietnam, Laos, and Cambodia Using Sentinel-1 Data

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

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