First Experience with Sentinel-2 Data for Crop and Tree Species Classifications in Central Europe

Immitzer, Markus; Vuolo, Francesco; Atzberger, Clement

doi:10.3390/rs8030166

Cited by 734 publications

(595 citation statements)

References 62 publications

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“…Considering maize and sugar beet alone only confirmed the SWIR band while Red-Edge 2 and 3 outperformed Red-Edge 1. Differences might be due to differences in the crop types of interest; crop classification results from Immitzer et al [16] were mainly driven by winter wheat and maize, which suggests that different spectral bands enhance different pairwise comparisons.…”

Section: Discussionmentioning

confidence: 99%

“…For crop and tree type identification, Immitzer et al [16] underlined the importance of Red-Edge 1, Blue and SWIR bands. Considering maize and sugar beet alone only confirmed the SWIR band while Red-Edge 2 and 3 outperformed Red-Edge 1.…”

Section: Discussionmentioning

confidence: 99%

“…Landsat-8 acquisitions are available from March 2013; Sentinel-2A acquisitions are available from end of November 2015. Thanks to its state-of-the-art specifications, Sentinel-2 [12,13] was designed for a variety of land monitoring applications such as water detection [14], mapping built-up areas [15] and crop type and tree species identification [16]. In addition to its spatial resolution, its payload offers thirteen spectral bands from Blue to SWIR, including Red-edge bands which have already proved to be useful for forest stress monitoring [17], land use and land cover mapping [18,19] and biophysical variable retrieval [20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

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Sentinel-2’s Potential for Sub-Pixel Landscape Feature Detection

et al. 2016

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Land cover and land use maps derived from satellite remote sensing imagery are critical to support biodiversity and conservation, especially over large areas. With its 10 m to 20 m spatial resolution, Sentinel-2 is a promising sensor for the detection of a variety of landscape features of ecological relevance. However, many components of the ecological network are still smaller than the 10 m pixel, i.e., they are sub-pixel targets that stretch the sensor’s resolution to its limit. This paper proposes a framework to empirically estimate the minimum object size for an accurate detection of a set of structuring landscape foreground/background pairs. The developed method combines a spectral separability analysis and an empirical point spread function estimation for Sentinel-2. The same approach was also applied to Landsat-8 and SPOT-5 (Take 5), which can be considered as similar in terms of spectral definition and spatial resolution, respectively. Results show that Sentinel-2 performs consistently on both aspects. A large number of indices have been tested along with the individual spectral bands and target discrimination was possible in all but one case. Overall, results for Sentinel-2 highlight the critical importance of a good compromise between the spatial and spectral resolution. For instance, the Sentinel-2 roads detection limit was of 3 m and small water bodies are separable with a diameter larger than 11 m. In addition, the analysis of spectral mixtures draws attention to the uneven sensitivity of a variety of spectral indices. The proposed framework could be implemented to assess the fitness for purpose of future sensors within a large range of applications.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Sentinel-2’s Potential for Sub-Pixel Landscape Feature Detection

et al. 2016

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“…In our study, we applied VHR RapidEye data comprising a red edge channel that was centered at 710 nm. Recent studies have shown the performance of red edge bands in facilitating important information on the state of vegetation and their benefit for vegetation monitoring [29][30][31]. In 2015, Sentinel-2A satellite has been launched.…”

Section: Introductionmentioning

confidence: 99%

Windthrow Detection in European Forests with Very High-Resolution Optical Data

Einzmann

Immitzer

Böck

et al. 2017

Forests

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With climate change, extreme storms are expected to occur more frequently. These storms can cause severe forest damage, provoking direct and indirect economic losses for forestry. To minimize economic losses, the windthrow areas need to be detected fast to prevent subsequent biotic damage, for example, related to beetle infestations. Remote sensing is an efficient tool with high potential to cost-efficiently map large storm affected regions. Storm Niklas hit South Germany in March 2015 and caused widespread forest cover loss. We present a two-step change detection approach applying commercial very high-resolution optical Earth Observation data to spot forest damage. First, an object-based bi-temporal change analysis is carried out to identify windthrow areas larger than 0.5 ha. For this purpose, a supervised Random Forest classifier is used, including a semi-automatic feature selection procedure; for image segmentation, the large-scale mean shift algorithm was chosen. Input features include spectral characteristics, texture, vegetation indices, layer combinations and spectral transformations. A hybrid-change detection approach at pixel-level subsequently identifies small groups of fallen trees, combining the most important features of the previous processing step with Spectral Angle Mapper and Multivariate Alteration Detection. The methodology was evaluated on two test sites in Bavaria with RapidEye data at 5 m pixel resolution. The results regarding windthrow areas larger than 0.5 ha were validated with reference data from field visits and acquired through orthophoto interpretation. For the two test sites, the novel object-based change detection approach identified over 90% of the windthrow areas (≥0.5 ha). The red edge channel was the most important for windthrow identification. Accuracy levels of the change detection at tree level could not be calculated, as it was not possible to collect field data for single trees, nor was it possible to perform an orthophoto validation. Nevertheless, the plausibility and applicability of the pixel-based approach is demonstrated on a second test site.

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“…Thus, crop mapping based on satellite data desperately requires high-quality spatial and temporal data, particularly in China where agricultural landscapes are complex with a diversity of cropping systems and small field sizes [18]. New sensors, such as Chinese GaoFen and overseas Sentinel-2A sensor, were designed for data enhancement to offer unprecedented perspectives on land cover/use and vegetation [19][20][21][22]. These new missions revisit the same area more frequently (every four or ten days) [23,24].…”

Section: Introductionmentioning

confidence: 99%

In-Season Crop Mapping with GF-1/WFV Data by Combining Object-Based Image Analysis and Random Forest

Song

Zhou

et al. 2017

Remote Sensing

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Producing accurate crop maps during the current growing season is essential for effective agricultural monitoring. Substantial efforts have been made to study regional crop distribution from year to year, but less attention is paid to the dynamics of composition and spatial extent of crops within a season. Understanding how crops are distributed at the early developing stages allows for the timely adjustment of crop planting structure as well as agricultural decision making and management. To address this knowledge gap, this study presents an approach integrating object-based image analysis with random forest (RF) for mapping in-season crop types based on multi-temporal GaoFen satellite data with a spatial resolution of 16 meters. A multiresolution local variance strategy was used to create crop objects, and then object-based spectral/textural features and vegetation indices were extracted from those objects. The RF classifier was employed to identify different crop types at four crop growth seasons by integrating available features. The crop classification performance of different seasons was assessed by calculating F-score values. Results show that crop maps derived using seasonal features achieved an overall accuracy of more than 87%. Compared to the use of spectral features, a feature combination of in-season textures and multi-temporal spectral and vegetation indices performs best when classifying crop types. Spectral and temporal information is more important than texture features for crop mapping. However, texture can be essential information when there is insufficient spectral and temporal information (e.g., crop identification in the early spring). These results indicate that an object-based image analysis combined with random forest has considerable potential for in-season crop mapping using high spatial resolution imagery.

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First Experience with Sentinel-2 Data for Crop and Tree Species Classifications in Central Europe

Cited by 734 publications

References 62 publications

Sentinel-2’s Potential for Sub-Pixel Landscape Feature Detection

Sentinel-2’s Potential for Sub-Pixel Landscape Feature Detection

Windthrow Detection in European Forests with Very High-Resolution Optical Data

In-Season Crop Mapping with GF-1/WFV Data by Combining Object-Based Image Analysis and Random Forest

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