Potential of Different Optical and SAR Data in Forest and Land Cover Classification to Support REDD+ MRV

Sirro, Laura; Häme, Tuomas; Rauste, Yrjö; Kilpi, Jorma; Hämäläinen, Jarno; Gunia, Katja; Jong, B. de; Pellat, Fernando Paz

doi:10.3390/rs10060942

Cited by 24 publications

(21 citation statements)

References 37 publications

(60 reference statements)

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“…There are a number of recent studies that argue in support of a combined use of SAR and optical data for tropical forest monitoring [3,[50][51][52][53]. Recent results indicate that a combined use can improve tropical forest monitoring for burnt area detection [14], for forest/non-forest mapping [52,54,55], for biomass assessment [56][57][58], and for deforestation and degradation monitoring [45,51,59]. A combination of ALOS PALSAR data and Landsat data was also used to enhance the discrimination of mature forest, secondary forest, and non-forest areas [60].…”

Section: Forest Disturbance Monitoring Combining Sar and Optical Datamentioning

confidence: 99%

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

et al. 2020

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Frequent cloud cover and fast regrowth often hamper topical forest disturbance monitoring with optical data. This study aims at overcoming these limitations by combining dense time series of optical (Sentinel-2 and Landsat 8) and SAR data (Sentinel-1) for forest disturbance mapping at test sites in Peru and Gabon. We compare the accuracies of the individual disturbance maps from optical and SAR time series with the accuracies of the combined map. We further evaluate the detection accuracies by disturbance patch size and by an area-based sampling approach. The results show that the individual optical and SAR based forest disturbance detections are highly complementary, and their combination improves all accuracy measures. The overall accuracies increase by about 3% in both areas, producer accuracies of the disturbed forest class increase by up to 25% in Peru when compared to only using one sensor type. The assessment by disturbance patch size shows that the amount of detections of very small disturbances (< 0.2 ha) can almost be doubled by using both data sets: for Gabon 30% as compared to 15.7–17.5%, for Peru 80% as compared to 48.6–65.7%.

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Section: Forest Disturbance Monitoring Combining Sar and Optical Datamentioning

confidence: 99%

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

et al. 2020

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“…The RF disturbance probability model of Sentinel-1 had a low PA of disturbance (58.6%), indicating large omission errors of disturbance. This result was not surprising because C-band SAR is generally less sensitive to forest structures [42,43]. Although we applied careful preprocessing to remove noise from the SAR backscatter coefficients, such as terrain correction, the mountainous regions in the study area might have affected the accuracy of detection, because rugged terrain affects the geometric and radiometric conditions of SAR observations.…”

Section: Discussionmentioning

confidence: 95%

“…Sentinel-1 data have been used for crop mapping [36,37], land cover classification [38][39][40] and forest monitoring [41]. Although shorter wavelength (C-band) SAR data is usually unsuitable for estimating forest structures [42,43], several studies have reported encouraging results using Sentinel-1 time series data for detecting disturbances and mapping deforestation in tropical regions [34,44].…”

Section: Introductionmentioning

confidence: 99%

Detecting Forest Changes Using Dense Landsat 8 and Sentinel-1 Time Series Data in Tropical Seasonal Forests

2019

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The accurate and timely detection of forest disturbances can provide valuable information for effective forest management. Combining dense time series observations from optical and synthetic aperture radar satellites has the potential to improve large-area forest monitoring. For various disturbances, machine learning algorithms might accurately characterize forest changes. However, there is limited knowledge especially on the use of machine learning algorithms to detect forest disturbances through hybrid approaches that combine different data sources. This study investigated the use of dense Landsat 8 and Sentinel-1 time series data for detecting disturbances in tropical seasonal forests based on a machine learning algorithm. The random forest algorithm was used to predict the disturbance probability of each Landsat 8 and Sentinel-1 observation using variables derived from a harmonic regression model, which characterized seasonality and disturbance-related changes. The time series disturbance probabilities of both sensors were then combined to detect forest disturbances in each pixel. The results showed that the combination of Landsat 8 and Sentinel-1 achieved an overall accuracy of 83.6% for disturbance detection, which was higher than the disturbance detection using only Landsat 8 (78.3%) or Sentinel-1 (75.5%). Additionally, more timely disturbance detection was achieved by combining Landsat 8 and Sentinel-1. Small-scale disturbances caused by logging led to large omissions of disturbances; however, other disturbances were detected with relatively high accuracy. Although disturbance detection using only Sentinel-1 data had low accuracy in this study, the combination with Landsat 8 data improved the accuracy of detection, indicating the value of dense Landsat 8 and Sentinel-1 time series data for timely and accurate disturbance detection.

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“…As reference data set, three Pléiades images, two from 19 and one from 21 November 2016 (© Centre National D'Etudes Spatiales (CNES)/AirbusDS) in 50 cm resolution and a SPOT-5 scene (©CNES) dated 25 June 2015 in 5 m resolution from the SPOT5-Take5 program were made available through ESA and the Centre National D'Etudes Spatiales (CNES) (Figure 9). We follow the approach of [47] based on a systematic sampling grid. As we have trained the MLC with in situ knowledge, RapidEye imagery from 2013, and GoogleEarth, the SPOT5 and Pléiades VHR data are independent and solely used for validation.…”

Section: Validation and Inter-comparison Approachmentioning

confidence: 99%

Multi-Temporal and Multi-Frequency SAR Analysis for Forest Land Cover Mapping of the Mai-Ndombe District (Democratic Republic of Congo)

Haarpaintner

Hindberg

2019

Remote Sensing

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The European Space Agency's (ESA) "SAR for REDD" project aims to support complementing optical remote sensing capacities in Africa with synthetic aperture radar (SAR) for Reducing Emissions from Deforestation and Forest Degradation (REDD). The aim of this study is to assess and compare Sentinel-1 C-band, ALOS-2 PALSAR-2 L-band and combined C/L-band SAR-based land cover mapping over a large tropical area in the Democratic Republic of Congo (DRC). The overall approach is to benefit from multi-temporal observations acquired from 2015 to 2017 to extract statistical parameters and seasonality of backscatters to improve forest land cover (FLC) classification. We investigate whether and to what extent the denser time series of C-band SAR can compensate for the L-band's deeper vegetation penetration depth and known better FLC mapping performance. The supervised classification differentiates into forest, inundated forest, woody savannah, dry and wet grassland, and river swamps. Several feature combinations of statistical parameters from both, single and multi-frequency observations in a multivariate maximum-likelihood classification are compared. The FLC maps are reclassified into forest, savannah, and grassland (FSG) and validated with a systematic sampling grid of manual interpretations of very-high-resolution optical satellite data. Using the temporal variability of the dual-polarized backscatters, in the form of either wet/dry seasonal averages or using the statistical variance, in addition to the average backscatter, increased the classification accuracies by 4-5 percent points and 1-2 percent points for C-and L-band, respectively. For the FSG validation overall accuracies of 84.4%, 89.1%, and 90.0% were achieved for single frequency C-and L-band, and C/L-band combined, respectively. The resulting forest/non-forest (FNF) maps with accuracies of 90.3%, 92.2%, and 93.3%, respectively, are then compared to the Landsat-based Global Forest Change program's and JAXA's ALOS-1/2 based global FNF maps.

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Potential of Different Optical and SAR Data in Forest and Land Cover Classification to Support REDD+ MRV

Cited by 24 publications

References 37 publications

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

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

Detecting Forest Changes Using Dense Landsat 8 and Sentinel-1 Time Series Data in Tropical Seasonal Forests

Multi-Temporal and Multi-Frequency SAR Analysis for Forest Land Cover Mapping of the Mai-Ndombe District (Democratic Republic of Congo)

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