Evaluation of Using Sentinel-1 and -2 Time-Series to Identify Winter Land Use in Agricultural Landscapes

Denize, Julien; Hubert‐Moy, Laurence; Betbeder, Julie; Corgne, Samuel; Baudry, Jacques; Pottier, Éric

doi:10.3390/rs11010037

Cited by 70 publications

(70 citation statements)

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“…The importance of vegetation cover during winter to preserve soil quality and water resources is now well-recognized by scientists, decision makers and citizens, and land-use mapping is considered a relevant input into decision-making to implement appropriate policy responses [1]. However, although identifying land use in agricultural areas is a major environmental and scientific issue [2], it remains challenging due to its high spatio-temporal dynamics [3]. In this context, remotely sensed time-series data are a valuable tool to identify land use by providing precise and timely information about the phenological status and development of vegetation at different scales, from local to global extents [4,5].…”

Section: Introductionmentioning

confidence: 99%

“…In the past few decades, progress has been made with the development of high and very high spatial and temporal resolution optical (e.g., Satellite pour l'observation de la Terre (SPOT-6/7), Sentinel-2) and Synthetic Aperture Radar (SAR) (e.g., TerraSAR-X, RADARSAT-2, Advanced Land Observing Satellite 2 (ALOS-2), Sentinel-1) sensors [6][7][8]. However, using optical time-series to identify land use in winter is limited by cloud cover and/or low solar irradiance [9], and late winter is a critical period during which vegetation begins to grow [3]. Conversely, SAR time-series provide a reliable solution to address the limitations of optical images because they are not sensitive to atmospheric conditions and can operate day and night [10].…”

Section: Introductionmentioning

confidence: 99%

“…25% of the UAA and include a wide variety of crops • Crop residues, which cover ca. 5% of the UAA and correspond to maize stalks that are left in fields when the maize is harvested after 1 November [3] Field observations were conducted in 231 crop fields monthly from November 2016 to February 2017 to calibrate and validate classification of remote sensing data ( Figure 1). Samples were randomly distributed throughout the study site, with half of the fields (116) being used for training and the other half for validation.…”

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confidence: 99%

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Polarimetric SAR Time-Series for Identification of Winter Land Use

Denize

Hubert‐Moy

Pottier

2019

Sensors

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In the past decade, high spatial resolution Synthetic Aperture Radar (SAR) sensors have provided information that contributed significantly to cropland monitoring. However, the specific configurations of SAR sensors (e.g., band frequency, polarization mode) used to identify land-use types remains underexplored. This study investigates the contribution of C/L-Band frequency, dual/quad polarization and the density of image time-series to winter land-use identification in an agricultural area of approximately 130 km² located in northwestern France. First, SAR parameters were derived from RADARSAT-2, Sentinel-1 and Advanced Land Observing Satellite 2 (ALOS-2) time-series, and one quad-pol and six dual-pol datasets with different spatial resolutions and densities were calculated. Then, land use was classified using the Random Forest algorithm with each of these seven SAR datasets to determine the most suitable SAR configuration for identifying winter land-use. Results highlighted that (i) the C-Band (F1-score 0.70) outperformed the L-Band (F1-score 0.57), (ii) quad polarization (F1-score 0.69) outperformed dual polarization (F1-score 0.59) and (iii) a dense Sentinel-1 time-series (F1-score 0.70) outperformed RADARSAT-2 and ALOS-2 time-series (F1-score 0.69 and 0.29, respectively). In addition, Shannon Entropy and SPAN were the SAR parameters most important for discriminating winter land-use. Thus, the results of this study emphasize the interest of using Sentinel-1 time-series data for identifying winter land-use.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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Polarimetric SAR Time-Series for Identification of Winter Land Use

Denize

Hubert‐Moy

Pottier

2019

Sensors

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“…In addition, machine learning algorithms are increasingly being used for data and image analysis [52,62,[72][73][74][75][76][77][78][79]. The CNN applied in the current study was tested to classify single black locust images under varying conditions and attained a high test accuracy of 99.5%.…”

Section: Discussionmentioning

confidence: 98%

“…Furthermore, there is an increasing interest in machine learning algorithms for data and image analysis, such as the application of the random forest model [52,[72][73][74][75][76], support vector machine [73][74][75][76], and deep learning algorithms, especially convolutional neural networks (CNNs) [62,73,75,[77][78][79]. However, CNNs were not previously utilized for the classification of black locust in short rotation coppices under varying conditions in single images.…”

Section: Introductionmentioning

confidence: 99%

Robinia pseudoacacia L. in Short Rotation Coppice: Seed and Stump Shoot Reproduction as well as UAS-based Spreading Analysis

Carl

Lehmann²,

Landgraf

et al. 2019

Forests

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Varying reproduction strategies are an important trait that tree species need in order both to survive and to spread. Black locust is able to reproduce via seeds, stump shoots, and root suckers. However, little research has been conducted on the reproduction and spreading of black locust in short rotation coppices. This research study focused on seed germination, stump shoot resprout, and spreading by root suckering of black locust in ten short rotation coppices in Germany. Seed experiments and sample plots were analyzed for the study. Spreading was detected and measured with unmanned aerial system (UAS)-based images and classification technology-object-based image analysis (OBIA). Additionally, the classification of single UAS images was tested by applying a convolutional neural network (CNN), a deep learning model. The analyses showed that seed germination increases with increasing warm-cold variety and scarification. Moreover, it was found that the number of shoots per stump decreases as shoot age increases. Furthermore, spreading increases with greater light availability and decreasing tillage. The OBIA and CNN image analysis technologies achieved 97% and 99.5% accuracy for black locust classification in UAS images. All in all, the three reproduction strategies of black locust in short rotation coppices differ with regards to initialization, intensity, and growth performance, but all play a role in the survival and spreading of black locust.Forests 2019, 10, 235 2 of 23 energy policies aimed towards a reduction in the use of fossil fuels and greater utilization of renewable energy since 2004 [7] marked an increase in fast-growing tree production, including black locust in short rotation coppices (harvested every 2-20 years) [8,9]. Nevertheless, some studies have declared black locust to be an invasive non-native tree species in Europe [6,[10][11][12]. However, other studies consider black locust to be an alternative tree species, for example for European ash (Fraxinus excelsior) [13,14], whose high dieback is caused by the fungal pathogen Hymenoscyphus fraxineus [14].Black locust possesses many growth characteristics that makes it ideal for short-rotation biomass production such as: rapid growth, high drought tolerance, and nitrogen-fixation [15,16], as well as the ability to reproduce via stump shoots in response to harvest. Black locust starts flowering at the age of six years [17]. Many insects benefit from this characteristic, including honeybees [18]. Moreover, the production of black locust honey is very common and economically important, especially in Hungary [17]. A maximum of 15,559 flowers per tree were counted in an eight-year-old black locust plantation [18], and 12,000 seeds/m 2 were identified in a monodominant stand [6,19]; black locust thus produces an abundance of seeds. As black locust belongs to the Fabaceae family, the seed coat is hard and impermeable [20,21]. Hence, seeds require scarification for successful germination [21][22][23] and priming of seeds is favorable [21,24,25]. Seeds pre...

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