Forest Area Derivation From Sentinel-1 Data

Dostálová, Alena; Hollaus, Markus; Milenković, Milutin; Wagner, Wolfgang

doi:10.5194/isprsannals-iii-7-227-2016

Cited by 18 publications

(8 citation statements)

References 13 publications

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“…Mapping approaches based on Sentinel-1 SAR (time series) data have been proposed for classifying urban areas [3], general land cover classes [4,5]; forests [6][7][8][9][10]; water bodies or flooding [11][12][13] and croplands/grasslands [14,15]. Also, some recent studies on tropical forest monitoring are based on L-band data (e.g., [16,17]).…”

Section: Introductionmentioning

confidence: 99%

Combined Use of Optical and Synthetic Aperture Radar Data for REDD+ Applications in Malawi

Hirschmugl

Sobe

Deutscher

et al. 2018

Land

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Recent developments in satellite data availability allow tropical forest monitoring to expand in two ways: (1) dense time series foster the development of new methods for mapping and monitoring dry tropical forests and (2) the combination of optical data and synthetic aperture radar (SAR) data reduces the problems resulting from frequent cloud cover and yields additional information. This paper covers both issues by analyzing the possibilities of using optical (Sentinel-2) and SAR (Sentinel-1) time series data for forest and land cover mapping for REDD+ (Reducing Emissions from Deforestation and Forest Degradation) applications in Malawi. The challenge is to combine these different data sources in order to make optimal use of their complementary information content. We compare the results of using different input data sets as well as of two methods for data combination. Results show that time-series of optical data lead to better results than mono-temporal optical data (+8% overall accuracy for forest mapping). Combination of optical and SAR data leads to further improvements: +5% in overall accuracy for land cover and +1.5% for forest mapping. With respect to the tested combination methods, the data-based combination performs slightly better (+1% overall accuracy) than the result-based Bayesian combination.

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Section: Introductionmentioning

confidence: 99%

Combined Use of Optical and Synthetic Aperture Radar Data for REDD+ Applications in Malawi

Hirschmugl

Sobe

Deutscher

et al. 2018

Land

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“…Forest area mapping for relatively small areas based on Sentinel-1 data was recently addressed in several studies. Overall accuracies of 94% were found over study area in North-East China [31], 92% over study area in Lower Austria [30], and balanced accuracies between 80% and 93% were reported for six sites distributed worldwide [14]. Forest type (coniferous/broadleaf) classification using Sentinel-1 was tested in two test sites in Switzerland [15] with overall accuracy of 86%.…”

Section: Performance Of the Sentinel-1 Based Forest Mapsmentioning

confidence: 99%

“…For example, a study covering five areas from Alaska to Indonesia [14] revealed that the mean accuracy increases from 77% when using single Sentinel-1 scene to 87% when using mean and standard deviation of VV and VH backscatter computed over one year of acquisitions. Similarly, overall accuracies of 92% were achieved over Austria when using parameters derived from the entire leaf-off season [30]. A different approach was introduced in [31], where only three Sentinel-1 acquisitions were used for forest mapping; these were, however, chosen to capture the conditions before, during and after the freezing period.…”

Section: Introductionmentioning

confidence: 99%

European Wide Forest Classification Based on Sentinel-1 Data

et al. 2021

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The constellation of two Sentinel-1 satellites provides an unprecedented coverage of Synthetic Aperture Radar (SAR) data at high spatial (20 m) and temporal (2 to 6 days over Europe) resolution. The availability of dense time series enables the analysis of the SAR temporal signatures and exploitation of these signatures for classification purposes. Frequent backscatter observations allow derivation of temporally filtered time series that reinforce the effect of changes in vegetation phenology by limiting the influence of short-term changes related to environmental conditions. Recent studies have already shown the potential of multitemporal Sentinel-1 data for forest mapping, forest type classification (coniferous or broadleaved forest) as well as for derivation of phenological variables at local to national scales. In the present study, we tested the viability of a recently published multi-temporal SAR classification method for continental scale forest mapping by applying it over Europe and evaluating the derived forest type and tree cover density maps against the European-wide Copernicus High Resolution Layers (HRL) forest datasets and national-scale forest maps from twelve countries. The comparison with the Copernicus HRL datasets revealed high correspondence over the majority of the European continent with overall accuracies of 86.1% and 73.2% for the forest/non-forest and forest type maps, respectively, and a Pearson correlation coefficient of 0.83 for tree cover density map. Moreover, the evaluation of both datasets against the national forest maps showed that the obtained accuracies of Sentinel-1 forest maps are almost within range of the HRL datasets. The Sentinel-1 forest/non-forest and forest type maps obtained average overall accuracies of 88.2% and 82.7%, respectively, as compared to 90.0% and 87.2% obtained by the Copernicus HRL datasets. This result is especially promising due to the facts that these maps can be produced with a high degree of automation and that only a single year of Sentinel-1 data is required as opposed to the Copernicus HRL forest datasets that are updated every three years.

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“…Previous studies have proved that mapping forests using SAR data was feasible and accurate [7,30,31]. In this study, Sentinel-1 SAR data were employed to produce the forest map as the baseline for further assessing the effect of textures on classifying rubber plantations from natural forests.…”

Section: Forests Mappingmentioning

confidence: 99%

Effect of Textural Features in Remote Sensed Data on Rubber Plantation Extraction at Different Levels of Spatial Resolution

Zhang

Huang

et al. 2020

Forests

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The expansion of rubber (Hevea brasiliensis) plantations has been a critical driver for the rapid transformation of tropical forests, especially in Thailand. Rubber plantation mapping provides basic information for surveying resources, updating forest subplot information, logging, and managing the forest. However, due to the diversity of stand structure, complexity of the forest growth environment, and the similarity of spectral characteristics between rubber trees and natural forests, it is difficult to discriminate rubber plantation from natural forest using only spectral information. This study evaluated the validity of textural features for rubber plantation recognition at different spatial resolutions using GaoFen-1 (GF-1), Sentinel-2, and Landsat 8 optical data. C-band Sentinel-1 10 m imagery was first used to map forests (including both rubber plantations and natural forests) and non-forests, then the pixels identified as forests in the Sentinel-1 imagery were compared with GF-1, Sentinel-2, and Landsat 8 images to separate rubber plantations and natural forest using two different approaches: a method based on spectral information characteristics only and a method combining spectral and textural features. In addition, we extracted textural features of different window sizes (3 × 3 to 31 × 31) and analyzed the influence of window size on the separability of rubber plantations and natural forests. Our major findings include: (1) the suitable texture extraction window sizes of GF-1, Sentinel-2, and Landsat 8 are 31 × 31, 11 × 11 to 15 × 15, and 3 × 3 to 7 × 7, respectively; (2) correlation (COR) is a robust textural feature in remote sensing images with different resolutions; and (3) compared with classification by spectral information only, the producer’s accuracy of rubber plantations based on GF-1, Sentinel-2, and Landsat 8 was improved by 8.04%, 9.44%, and 8.74%, respectively, and the user’s accuracy was increased by 4.63%, 4.54%, and 6.75%, respectively, when the textural features were introduced. These results demonstrate that the method combining textural features has great potential in delineating rubber plantations.

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Forest Area Derivation From Sentinel-1 Data

Cited by 18 publications

References 13 publications

Combined Use of Optical and Synthetic Aperture Radar Data for REDD+ Applications in Malawi

Combined Use of Optical and Synthetic Aperture Radar Data for REDD+ Applications in Malawi

European Wide Forest Classification Based on Sentinel-1 Data

Effect of Textural Features in Remote Sensed Data on Rubber Plantation Extraction at Different Levels of Spatial Resolution

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