scholarly journals Separability of Mowing and Ploughing Events on Short Temporal Baseline Sentinel-1 Coherence Time Series

2020 ◽  
Vol 12 (22) ◽  
pp. 3784
Author(s):  
Kaupo Voormansik ◽  
Karlis Zalite ◽  
Indrek Sünter ◽  
Tanel Tamm ◽  
Kalev Koppel ◽  
...  
Keyword(s):  
Time Series ◽  
Management Practices ◽  
Coherence Time ◽  
Sar Interferometry ◽  
Large Dataset ◽  
Wide Area Monitoring ◽  
Detection Algorithms ◽  
Optical Images ◽  
Area Monitoring ◽  
Sentinel 2

Short temporal baseline regular Synthetic Aperture Radar (SAR) interferometry is a tool well suited for wide area monitoring of agricultural activities, urgently needed in European Union Common Agricultural Policy (CAP) enforcement. In this study, we demonstrate and describe in detail, how mowing and ploughing events can be identified from Sentinel-1 6-day interferometric coherence time series. The study is based on a large dataset of 386 dual polarimetric Sentinel-1 VV/VH SAR and 351 Sentinel-2 optical images, and nearly 2000 documented mowing and ploughing events on more than 1000 parcels (average 10.6 ha, smallest 0.6 ha, largest 108.5 ha). Statistical analysis revealed that mowing and ploughing cause coherence to increase when compared to values before an event. In the case of mowing, the coherence increased from 0.18 to 0.35, while Sentinel-2 NDVI (indicating the amount of green chlorophyll containing biomass) at the same time decreased from 0.75 to 0.5. For mowing, there was virtually no difference between the polarisations. After ploughing, VV-coherence grew up to 0.65 and VH-coherence to 0.45, while NDVI was around 0.2 at the same time. Before ploughing, both coherence and NDVI values were very variable, determined by the agricultural management practices of the parcel. Results presented here can be used for planning further studies and developing mowing and ploughing detection algorithms based on Sentinel-1 data. Besides CAP enforcement, the results are also useful for food security and land use change detection applications.

scholarly journals Time-Series of Vegetation Indices (VNIR/SWIR) Derived from Sentinel-2 (A/B) to Assess Turgor Pressure in Kiwifruit

2020 ◽  
Vol 9 (11) ◽  
pp. 641
Author(s):  
Alberto Jopia ◽  
Francisco Zambrano ◽  
Waldo Pérez-Martínez ◽  
Paulina Vidal-Páez ◽  
Julio Molina ◽  
...  
Keyword(s):  
Time Series ◽  
Crop Production ◽  
Management Practices ◽  
Water Status ◽  
Vegetation Indices ◽  
Turgor Pressure ◽  
Security Risk ◽  
Vegetative Development ◽  
Water Sensor ◽  
Sentinel 2

For more than ten years, Central Chile has faced drought conditions, which impact crop production and quality, increasing food security risk. Under this scenario, implementing management practices that allow increasing water use efficiency is urgent. The study was carried out on kiwifruit trees, located in the O’Higgins region, Chile for season 2018–2019 and 2019–2020. We evaluate the time-series of nine vegetation indices in the VNIR and SWIR regions derived from Sentinel-2 (A/B) satellites to establish how much variability in the canopy water status there was. Over the study’s site, eleven sensors were installed in five trees, which continuously measured the leaf’s turgor pressure (Yara Water-Sensor). A strong Spearman’s (ρ) correlation between turgor pressure and vegetation indices was obtained, having −0.88 with EVI and −0.81 with GVMI for season 2018–2019, and lower correlation for season 2019–2020, reaching −0.65 with Rededge1 and −0.66 with EVI. However, the NIR range’s indices were influenced by the vegetative development of the crop rather than its water status. The red-edge showed better performance as the vegetative growth did not affect it. It is necessary to expand the study to consider higher variability in kiwifruit’s water conditions and incorporate the sensitivity of different wavelengths.

scholarly journals Mapping Winter Crops in China with Multi-Source Satellite Imagery and Phenology-Based Algorithm

2019 ◽  
Vol 11 (7) ◽  
pp. 820 ◽  
Author(s):  
Haifeng Tian ◽  
Ni Huang ◽  
Zheng Niu ◽  
Yuchu Qin ◽  
Jie Pei ◽  
...  
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Time Windows ◽  
Vegetation Indices ◽  
Optical Images ◽  
Landsat 7 ◽  
Multi Temporal ◽  
Winter Crop ◽  
Winter Crops ◽  
Sentinel 2

Timely and accurate mapping of winter crop planting areas in China is important for food security assessment at a national level. Time-series of vegetation indices, such as the normalized difference vegetation index (NDVI), are widely used for crop mapping, as they can characterize the growth cycle of crops. However, with the moderate spatial resolution optical imagery acquired by Landsat and Sentinel-2, it is difficult to obtain complete time-series curves for vegetation indices due to the influence of the revisit cycle of the satellite and weather conditions. Therefore, in this study, we propose a method for compositing the multi-temporal NDVI, in order to map winter crop planting areas with the Landsat-7 and -8 and Sentinel-2 optical images. The algorithm composites the multi-temporal NDVI into three key values, according to two time-windows—a period of low NDVI values and a period of high NDVI values—for the winter crops. First, we identify the two time-windows, according to the time-series of the NDVI obtained from daily Moderate Resolution Imaging Spectroradiometer observations. Second, the 30 m spatial resolution multi-temporal NDVI curve, derived from the Landsat-7 and -8 and Sentinel-2 optical images, is composited by selecting the maximal value in the high NDVI value period, and the minimal and median values in the low NDVI value period, using an algorithm of the Google Earth Engine. Third, a decision tree classification method is utilized to perform the winter crop classification at a pixel level. The results indicate that this method is effective for the large-scale mapping of winter crops. In the study area, the area of winter crops in 2018 was determined to be 207,641 km2, with an overall accuracy of 96.22% and a kappa coefficient of 0.93. The method proposed in this paper is expected to contribute to the rapid and accurate mapping of winter crops in large-scale applications and analyses.

Forest Disturbance Mapping with Sentinel-2 Time Series in Austria

2021 ◽  
Author(s):  
Markus Löw ◽  
Tatjana Koukal
Keyword(s):  
Time Series ◽  
Forest Management ◽  
Time Series Analysis ◽  
Bark Beetle ◽  
Management Practices ◽  
Forest Ecosystems ◽  
Forest Disturbance ◽  
Spatial Scales ◽  
Forest Disturbances ◽  
Sentinel 2

<p>Worldwide, forests provide natural resources and ecosystem services. However, forest ecosystems are threatened by increasing forest disturbance dynamics, caused by direct human activities or by altering environmental conditions. It is decisive to reconstruct and trace the intra- to transannual dynamics of forest ecosystems. Therefore, the monitoring of large and small scale vegetation changes such as those caused by natural events (e.g., pest infestation, higher mortality due to altering site conditions) or forest management practices (e.g., thinning or selective timber extraction) becomes more and more crucial. National to local forest authorities and other stakeholders request detailed area-wide maps that delineate forest disturbance dynamics at various spatial scales.</p><p>We developed a time series analysis (TSA) framework that comprises data download, data management, image preprocessing and an advanced but flexible TSA. We use dense Sentinel-2 time series and a dynamic Savitzky–Golay-filtering approach to model robust but sensitive phenology courses. Deviations from the phenology models are used to derive detailed spatiotemporal information on forest disturbances. In a first case study, we apply the TSA to map forest disturbances directly or indirectly linked to recurring bark beetle infestation in Northern Austria.</p><p>In addition to spatiotemporal disturbance maps, we produce zonal statistics on different spatial scales that provide aggregated information on the extent of forest disturbances between 2018 and 2019. The outcomes are (a) area-wide consistent data of individual phenology models and deduced phenology metrics for Austrian forests and (b) operational forest disturbance maps, useful to investigate and monitor forest disturbances, for example to facilitate sustainable forest management.</p><p>At a forest stand level, we reconstruct the origin date of forest disturbances (FDD – Forest Disturbance Date). Theses FDD outputs show the spatiotemporal patterns and the development of damages and indicate that most dynamics are caused by recurring and spreading bark beetle infestation. The validation results based on field data confirm a high detection rate and show that the derived temporal information is reliable. In total, 23400 hectares, i.e., on average 2.8% of the forest area in the study area, are found to be affected by forest disturbance. The zonal statistic maps point out hotspots of significant forest disturbances, where adequate forest management measures are highly needed. Furthermore, this study highlights the TSA’s potential to also depict and monitor minor human impacts on forests, such as thinning, selective timber extraction or other moderate forest management practices.</p><p><strong>Keywords:  </strong><em>forest disturbance; forest monitoring; bark beetle infestation; forest management; time series analysis; phenology modelling; remote sensing; satellite imagery; Sentinel-2</em></p>

scholarly journals Mapping Paddy Fields in Japan by Using a Sentinel-1 SAR Time Series Supplemented by Sentinel-2 Images on Google Earth Engine

2020 ◽  
Vol 12 (10) ◽  
pp. 1622 ◽  
Author(s):  
Shimpei Inoue ◽  
Akihiko Ito ◽  
Chinatsu Yonezawa
Keyword(s):  
Time Series ◽  
Paddy Field ◽  
Water Resource Management ◽  
Mapping Method ◽  
Google Earth ◽  
Paddy Fields ◽  
Optical Images ◽  
Sar Data ◽  
Decision Tree Method ◽  
Sentinel 2

Paddy fields play very important environmental roles in food security, water resource management, biodiversity conservation, and climate change. Therefore, reliable broad-scale paddy field maps are essential for understanding these issues related to rice and paddy fields. Here, we propose a novel paddy field mapping method that uses Sentinel-1 synthetic aperture radar (SAR) time series that are robust for cloud cover, supplemented by Sentinel-2 optical images that are more reliable than SAR data for extracting irrigated paddy fields. Paddy fields were provisionally specified by using the Sentinel-1 SAR data and a conventional decision tree method. Then, an additional mask using water and vegetation indexes based on Sentinel-2 optical images was overlaid to remove non-paddy field areas. We used the proposed method to develop a paddy field map for Japan in 2018 with a 30 m spatial resolution. The producer’s accuracy of this map (92.4%) for non-paddy reference agricultural fields was much higher than that of a map developed by the conventional method (57.0%) using only Sentinel-1 data. Our proposed method also reproduced paddy field areas at the prefecture scale better than existing paddy field maps developed by a remote sensing approach.

scholarly journals Estimating VAIA Windstorm Damaged Forest Area in Italy Using Time Series Sentinel-2 Imagery and Continuous Change Detection Algorithms

Forests ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 680
Author(s):  
Francesca Giannetti ◽  
Matteo Pecchi ◽  
Davide Travaglini ◽  
Saverio Francini ◽  
Giovanni D’Amico ◽  
...  
Keyword(s):  
Time Series ◽  
Change Detection ◽  
Forest Area ◽  
Forest Monitoring ◽  
Bayesian Estimator ◽  
Validation Dataset ◽  
Continuous Change ◽  
Detection Algorithms ◽  
Change Detection Algorithms ◽  
Sentinel 2

Mapping forest disturbances is an essential component of forest monitoring systems both to support local decisions and for international reporting. Between the 28 and 29 October 2018, the VAIA storm hit the Northeast regions of Italy with wind gusts exceeding 200 km h−1. The forests in these regions have been seriously damaged. Over 490 Municipalities in six administrative Regions in Northern Italy registered forest damages caused by VAIA, that destroyed or intensely damaged forest stands spread over an area of 67,000 km2. The present work tested the use of two continuous change detection algorithms, i.e., the Bayesian estimator of abrupt change, seasonal change, and trend (BEAST) and the continuous change detection and classification (CCDC) to map and estimate forest windstorm damage area using a normalized burned ration (NBR) time series calculated on three years Sentinel-2 (S2) images collection (i.e., January 2017–October 2019). We analyzed the accuracy of the maps and the damaged forest area using a probability-based stratified estimation within 12 months after the storm with an independent validation dataset. The results showed that close to the storm (i.e., 1 to 6 months November 2018–March 2019) it is not possible to obtain accurate results independently of the algorithm used, while accurate results were observed between 7 and 12 months from the storm (i.e., May 2019 – October 2019) in terms of Standard Error (SE), percentage SE (SE%), overall accuracy (OA), producer accuracy (PA), user accuracy (UA), and gmean for both BEAST and CCDC (SE< 3725.3 ha , SE% < 9.69 , OA > 89.7, PA and UA > 0.87, gmean > 0.83).

scholarly journals Earthquake-induced landslides monitoring and survey by means of InSAR

2021 ◽  
Author(s):  
Tayeb Smail ◽  
Mohamed Abed ◽  
Ahmed Mebarki ◽  
Milan Lazecky
Keyword(s):  
Time Series ◽  
Pilot Study ◽  
Proper Time ◽  
Line Of Sight ◽  
Sar Images ◽  
Ground Deformations ◽  
Site Investigations ◽  
Optical Images ◽  
Interferometric Sar ◽  
Sentinel 2

Abstract. This study uses interferometric SAR techniques to identify landslides and lands prone to landslides, detect fringes and changes in areas struck by earthquakes. The pilot study investigates the Mila region (Algeria) which suffered significant landslides and structural damages (earthquake: Mw 5, 2020-08-07): the study checks ground deformations and tracks earthquake-induced landslides. DInSAR analysis shows normal interferograms, with atmospheric contribution, and slight fringes. However, it identifies many landslides, the most important (2.5 m displacement) being located in Kherba neighborhood, causing severe damages to dwellings. In addition, SAR images and optical images (Sentinel-2) confirm site investigations. Although in Grarem City, optical images could not detect any disorder, the DInSAR analysis detected some coherence decays and small fringes (3.94 km2 area). These unnoticed ground disorders were confirmed during fields inspection. Such results have key importance since they can serve as an alert to monitor the zone at the proper time. Furthermore, Displacement time series analysis of many interferograms (April 2015 to September 2020) using LiCSBAS were performed to investigate the pre-event conditions and precursors of the slopes instabilities., LiCSBAS detects a line-of-sight subsidence velocity of −110 mm/y in the back hillside of Kherba, and high displacement velocity at specific points in Grarem region.

Combination of high frequency (Sentinel-2) and high resolution (Pléiades) satellite images for the monitoring of clayey landslide reactivations, application to the Harmalière landslide (French Alps).

2020 ◽  
Author(s):  
Denis Jongmans ◽  
Sylvain Fiolleau ◽  
Gregory Bièvre ◽  
Guillaume Chambon ◽  
Pascal Lacroix
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
High Resolution ◽  
High Frequency ◽  
Satellite Images ◽  
Cross Correlation ◽  
Temperate Climate ◽  
Optical Images ◽  
Slow Moving ◽  
Sentinel 2

&lt;p&gt;Many regions of the world are exposed to landslides in clay deposits, which poses major problems for land management and population safety. In recent years, optical satellite imaging has emerged as a major and inexpensive tool for understanding and monitoring the kinematics of slow moving landslides, such as earthflows/earthslides, through easy access of data and reliable calibration.&lt;/p&gt;&lt;p&gt;The Sentinel-2 optical satellites provide a global coverage of land surfaces with a 5-day revisit time at the Equator. We studied the ability of these freely available optical images to detect landslide reactivations in a zone of 25 km&lt;sup&gt;2&lt;/sup&gt; around the Harmali&amp;#232;re landslide in the Tri&amp;#232;ves area (western Alps, France). This area is characterized by the presence of a thick lacustrine clay layer that is affected by numerous landslides. Using a 9-month time-series of displacement derived from Sentinel-2 data, Lacroix et al. 2018 recently evidenced a precursor displacement of a major reactivation of the Harmali&amp;#232;re landslide that occurred in June 2016.&lt;/p&gt;&lt;p&gt;In this study, we attempted to detect following reactivations using the medium resolution high frequency satellite images (Sentinel 2) coupled with high resolution images (Pl&amp;#233;iades) over a longer period (2016- 2019). We used an inversion strategy of redundant cross-correlation images to produce a robust time-series of displacement from Sentinel 2 data (Bontemps et al. 2018). By applying this technique, we were able to identify a reactivation of the same order of magnitude as the previous one, which affected the headscarp in January 2017. The reactivation signal is validated by the cross-correlation of Pl&amp;#233;iades images taken at 2 years interval. We quantified this reactivation in time and space. We have also identified an area of 30x10&lt;sup&gt;3&lt;/sup&gt; m2 located at the foot of the landslide, which was simultaneously accelerated by 10 m/month during this event. This information contributes to better understand the dynamics of the landslide that evolves from a solid to fluid behavior from the headscarp to the toe. However, a smaller slide that occurred in January 2018 at the headscarp was not detected by this method despite its significant size (10x10&lt;sup&gt;3&lt;/sup&gt; m&lt;sup&gt;2&lt;/sup&gt;). We attribute this non-detection to a major reshaping of the surface following reactivation.&lt;/p&gt;&lt;p&gt;This study identified the possibilities and limitations of the proposed treatment method to detect and monitor landslides on a low-slope area located in clayey soils in a temperate climate.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;Bontemps, N., Lacroix, P. &amp; Doin, M.-P. (2018) Inversion of deformation fields time-series from optical images, and application to the long term kinematics of slow-moving landslides in Peru. Remote Sensing of Environment, &lt;strong&gt;210&lt;/strong&gt;, 144&amp;#8211;158. doi:10.1016/j.rse.2018.02.023&lt;/p&gt;&lt;p&gt;Lacroix, P., Bi&amp;#232;vre, G., Pathier, E., Kniess, U. &amp; Jongmans, D. (2018) Use of Sentinel-2 images for the detection of precursory motions before landslide failures. Remote Sensing of Environment, &lt;strong&gt;215&lt;/strong&gt;, 507&amp;#8211;516. doi:10.1016/j.rse.2018.03.042&lt;/p&gt;

scholarly journals Mapping Irrigated Areas Using Sentinel-1 Time Series in Catalonia, Spain

2019 ◽  
Vol 11 (15) ◽  
pp. 1836 ◽  
Author(s):  
Hassan Bazzi ◽  
Nicolas Baghdadi ◽  
Dino Ienco ◽  
Mohammad El Hajj ◽  
Mehrez Zribi ◽  
...  
Keyword(s):  
Time Series ◽  
Water Resource Management ◽  
Vegetation Index ◽  
Scale Up ◽  
Principal Component ◽  
Radar Data ◽  
Temporal Series ◽  
Optical Images ◽  
Combined Use ◽  
Sentinel 2

Mapping irrigated plots is essential for better water resource management. Today, the free and open access Sentinel-1 (S1) and Sentinel-2 (S2) data with high revisit time offers a powerful tool for irrigation mapping at plot scale. Up to date, few studies have used S1 and S2 data to provide approaches for mapping irrigated plots. This study proposes a method to map irrigated plots using S1 SAR (synthetic aperture radar) time series. First, a dense temporal series of S1 backscattering coefficients were obtained at plot scale in VV (Vertical-Vertical) and VH (Vertical-Horizontal) polarizations over a study site located in Catalonia, Spain. In order to remove the ambiguity between rainfall and irrigation events, the S1 signal obtained at plot scale was used conjointly to S1 signal obtained at a grid scale (10 km × 10 km). Later, two mathematical transformations, including the principal component analysis (PCA) and the wavelet transformation (WT), were applied to the several SAR temporal series obtained in both VV and VH polarization. Irrigated areas were then classified using the principal component (PC) dimensions and the WT coefficients in two different random forest (RF) classifiers. Another classification approach using one dimensional convolutional neural network (CNN) was also performed on the obtained S1 temporal series. The results derived from the RF classifiers with S1 data show high overall accuracy using the PC values (90.7%) and the WT coefficients (89.1%). By applying the CNN approach on SAR data, a significant overall accuracy of 94.1% was obtained. The potential of optical images to map irrigated areas by the mean of a normalized differential vegetation index (NDVI) temporal series was also tested in this study in both the RF and the CNN approaches. The overall accuracy obtained using the NDVI in RF classifier reached 89.5% while that in the CNN reached 91.6%. The combined use of optical and radar data slightly enhanced the classification in the RF classifier but did not significantly change the accuracy obtained in the CNN approach using S1 data.

scholarly journals Potential of Sentinel-2 satellite images to monitor vine fields grown at a territorial scale

OENO One ◽  
2019 ◽  
Vol 53 (1) ◽  
Author(s):  
Nicolas Devaux ◽  
Thomas Crestey ◽  
Corentin Leroux ◽  
Bruno Tisseyre
Keyword(s):  
Time Series ◽  
Temporal Resolution ◽  
Vegetation Index ◽  
Management Practices ◽  
Regional Scale ◽  
Time Lag ◽  
Short Note ◽  
Ndvi Time Series ◽  
The Impact ◽  
Sentinel 2

Aim: The aim of this short note is to provide first insights into the ability of Sentinel-2 images to monitor vine growth across a whole season. It focuses on verifying the practical temporal resolution that can be reached with Sentinel-2 images, the main stages of Mediterranean vineyard development as well as potential relevant agronomic information that can be seen on the temporal vegetation curves arising from Sentinel-2 images.Methods and results: The study was carried out in 2017 in a production vineyard located in southern France, 2 km from the Mediterranean seashore. Sentinel-2 images acquired during the whole vine growing cycle were considered, i.e. between the 3rd of March 2017 and the 10th of October 2017. The images were used to compute the classical normalized difference vegetation index (NDVI). Time series of NDVI values were analyzed on four blocks chosen for exhibiting different features, e.g. age, missing plants, weeding practices. The practical time lag between two usable images was closer to 16 days than to the 10 theoretical days (with only one satellite available at the date of the experiment), i.e. near 60% of the theoretical one. Results show that it might be possible to identify i) the main steps of vine development (e.g. budburst, growth, trimming, growth stop and senescence), ii) weed management and inter-row management practices, and iii) possible reasons for significant inter-block differences in vegetative expression (e.g. young vines that have recently been planted, low-productive blocks affected by many missing vines).Conclusions: Although this experiment was conducted at a time when Sentinel-2b was not fully operational, results showed that a sufficient number of usable images was available to monitor vine development. The availability of two Sentinel satellites (2a and 2b) in upcoming seasons should increase the number of usable images and the temporal resolution of the time series. This study also showed the limitations of the Sentinel-2 images’ resolution to provide within-block information in the case of small blocks or blocks with complex borders or both.Significance and impact of the study: This technical note demonstrated the potential of Sentinel-2 images to characterize vineyard blocks’ vigor and to monitor winegrowers’ practices at a territorial (regional) scale. The impact of management operations such as weeding and trimming, along with their incidence on canopy size, were observed on the NDVI time series. Some relevant parameters (slope, maximum values) may be derived from the NDVI time series, providing new insights into the monitoring of vineyards at a large scale. These results provided areas for further investigation, especially regarding the development of new indicators to characterize block-climate relationships.

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