scholarly journals Calibrating Sentinel-2 Imagery with Multispectral UAV Derived Information to Quantify Damages in Mediterranean Rice Crops Caused by Western Swamphen (Porphyrio porphyrio)

Drones ◽  
2019 ◽  
Vol 3 (2) ◽  
pp. 45 ◽  
Author(s):  
Magda Pla ◽  
Gerard Bota ◽  
Andrea Duane ◽  
Jaume Balagué ◽  
Antoni Curcó ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Biological Diversity ◽  
Ground Truth ◽  
Western Mediterranean ◽  
Ebro Delta ◽  
Crop Damage ◽  
Pixel Resolution ◽  
Porphyrio Porphyrio ◽  
Sentinel 2

Making agricultural production compatible with the conservation of biological diversity is a priority in areas in which human–wildlife conflicts arise. The threatened Western Swamphen (Porphyrio porphyrio) feeds on rice, inducing crop damage and leading to decreases in rice production. Due to the Swamphen protection status, economic compensation policies have been put in place to compensate farmers for these damages, thus requiring an accurate, quantitative, and cost-effective evaluation of rice crop losses over large territories. We used information captured from a UAV (Unmanned Aerial Vehicle) equipped with a multispectral Parrot SEQUOIA camera as ground-truth information to calibrate Sentinel-2 imagery to quantify damages in the region of Ebro Delta, western Mediterranean. UAV vegetation index NDVI (Normalized Difference Vegetation Index) allowed estimation of damages in rice crops at 10 cm pixel resolution by discriminating no-green vegetation pixels. Once co-registered with Sentinel grid, we predicted the UAV damage proportion at a 10 m resolution as a function of Sentinel-2 NDVI, and then we extrapolated the fitted model to the whole Sentinel-2 Ebro Delta image. Finally, the damage predicted with Sentinel-2 data was quantified at the agricultural plot level and validated with field information compiled on the ground by Rangers Service. We found that Sentinel2-NDVI data explained up to 57% of damage reported with UAV. The final validation with Rangers Service data pointed out some limitations in our procedure that leads the way to improving future development. Sentinel2 imagery calibrated with UAV information proved to be a viable and cost-efficient alternative to quantify damages in rice crops at large scales.

scholarly journals Comparison and Ground Truthing of Different Remote and Proximal Sensing Platforms to Characterize Variability in a Hedgerow-Trained Vineyard

2021 ◽  
Vol 13 (11) ◽  
pp. 2056
Author(s):  
Cecilia Squeri ◽  
Stefano Poni ◽  
Salvatore Filippo Di Gennaro ◽  
Alessandro Matese ◽  
Matteo Gatti
Keyword(s):  
Spatial Resolution ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Growth Yield ◽  
Ground Truth ◽  
Large Degree ◽  
Proximal Sensing ◽  
Ground Truth Data ◽  
Ground Truthing ◽  
Sentinel 2

Appropriate characterization of intra-parcel variability is a key element for the effective application of precision farming techniques. Nowadays there are many platforms available to end users differing for pixel spatial resolution and the type of acquisition (remote or proximal). A challenging aspect pertaining to remote sensing image acquisition in the vineyard ecosystem is that, in a large majority of cases, vegetation is discontinuous and single rows alternate with strips of either bare or grassed soil. In this paper, four different satellite platforms (Sentinel-2, Spot-6, Pleiades, and WorldView-3) having different spatial resolution and MECS-VINE® proximity sensor were compared in terms of accuracy at describing spatial variability. Vineyard mapping was coupled with detailed ground truthing of growth, yield, and grape composition variables. The analysis was conducted based on vigor indices (Normalized Difference Vegetation Index or Canopy Index) and using the Moran Index (MI) to assess the degree of spatial auto-correlation for the different variables. The results obtained showed a large degree of intra-plot variability in the main agronomic parameters (pruning weight CV: 33.86%, yield: 32.09%). The univariate Moran index showed a log-linear function relating MI coefficients to the resolution levels. Comparison between vigor indices and agronomic data showed that the highest bivariate MI was reached by Pleiades followed by MECS-VINE® which also did not exhibit the negative effect of the border pixel owing to the proximal scanning acquisition. Despite WorldView-3′s high resolution (1.24 m pixel) allowing very detailed data imaging, the comparison with ground-truth data was not encouraging, probably due to the presence of pure ground pixels, while Sentinel-2 was affected by the oversized pixel at 10 m.

scholarly journals Mapping Public Urban Green Spaces Based on OpenStreetMap and Sentinel-2 Imagery Using Belief Functions

2021 ◽  
Vol 10 (4) ◽  
pp. 251
Author(s):  
Christina Ludwig ◽  
Robert Hecht ◽  
Sven Lautenbach ◽  
Martin Schorcht ◽  
Alexander Zipf
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Open Data ◽  
Green Spaces ◽  
Data Sets ◽  
Urban Green ◽  
Urban Green Spaces ◽  
Urban Quality Of Life ◽  
Shafer Theory ◽  
Sentinel 2

Public urban green spaces are important for the urban quality of life. Still, comprehensive open data sets on urban green spaces are not available for most cities. As open and globally available data sets, the potential of Sentinel-2 satellite imagery and OpenStreetMap (OSM) data for urban green space mapping is high but limited due to their respective uncertainties. Sentinel-2 imagery cannot distinguish public from private green spaces and its spatial resolution of 10 m fails to capture fine-grained urban structures, while in OSM green spaces are not mapped consistently and with the same level of completeness everywhere. To address these limitations, we propose to fuse these data sets under explicit consideration of their uncertainties. The Sentinel-2 derived Normalized Difference Vegetation Index was fused with OSM data using the Dempster–Shafer theory to enhance the detection of small vegetated areas. The distinction between public and private green spaces was achieved using a Bayesian hierarchical model and OSM data. The analysis was performed based on land use parcels derived from OSM data and tested for the city of Dresden, Germany. The overall accuracy of the final map of public urban green spaces was 95% and was mainly influenced by the uncertainty of the public accessibility model.

scholarly journals Modeling of Durum Wheat Yield Based on Sentinel-2 Imagery

Agronomy ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1486
Author(s):  
Chris Cavalaris ◽  
Sofia Megoudi ◽  
Maria Maxouri ◽  
Konstantinos Anatolitis ◽  
Marios Sifakis ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Model Development ◽  
Wheat Yield ◽  
Growth Period ◽  
Yield Data ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Sentinel 2

In this study, a modelling approach for the estimation/prediction of wheat yield based on Sentinel-2 data is presented. Model development was accomplished through a two-step process: firstly, the capacity of Sentinel-2 vegetation indices (VIs) to follow plant ecophysiological parameters was established through measurements in a pilot field and secondly, the results of the first step were extended/evaluated in 31 fields, during two growing periods, to increase the applicability range and robustness of the models. Modelling results were examined against yield data collected by a combine harvester equipped with a yield-monitoring system. Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) were examined as plant signals and combined with Normalized Difference Water Index (NDWI) and/or Normalized Multiband Drought Index (NMDI) during the growth period or before sowing, as water and soil signals, respectively. The best performing model involved the EVI integral for the 20 April–31 May period as a plant signal and NMDI on 29 April and before sowing as water and soil signals, respectively (R2 = 0.629, RMSE = 538). However, model versions with a single date and maximum seasonal VIs values as a plant signal, performed almost equally well. Since the maximum seasonal VIs values occurred during the last ten days of April, these model versions are suitable for yield prediction.

scholarly journals Outlier Detection at the Parcel-Level in Wheat and Rapeseed Crops Using Multispectral and SAR Time Series

2021 ◽  
Vol 13 (5) ◽  
pp. 956
Author(s):  
Florian Mouret ◽  
Mohanad Albughdadi ◽  
Sylvie Duthoit ◽  
Denis Kouamé ◽  
Guillaume Rieu ◽  
...  
Keyword(s):  
Outlier Detection ◽  
Experimental Validation ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Multispectral Images ◽  
Vegetation Indexes ◽  
Crop Development ◽  
Different Types ◽  
Selection Of ◽  
Sentinel 2

This paper studies the detection of anomalous crop development at the parcel-level based on an unsupervised outlier detection technique. The experimental validation is conducted on rapeseed and wheat parcels located in Beauce (France). The proposed methodology consists of four sequential steps: (1) preprocessing of synthetic aperture radar (SAR) and multispectral images acquired using Sentinel-1 and Sentinel-2 satellites, (2) extraction of SAR and multispectral pixel-level features, (3) computation of parcel-level features using zonal statistics and (4) outlier detection. The different types of anomalies that can affect the studied crops are analyzed and described. The different factors that can influence the outlier detection results are investigated with a particular attention devoted to the synergy between Sentinel-1 and Sentinel-2 data. Overall, the best performance is obtained when using jointly a selection of Sentinel-1 and Sentinel-2 features with the isolation forest algorithm. The selected features are co-polarized (VV) and cross-polarized (VH) backscattering coefficients for Sentinel-1 and five Vegetation Indexes for Sentinel-2 (among us, the Normalized Difference Vegetation Index and two variants of the Normalized Difference Water). When using these features with an outlier ratio of 10%, the percentage of detected true positives (i.e., crop anomalies) is equal to 94.1% for rapeseed parcels and 95.5% for wheat parcels.

scholarly journals Evaluation of the Consistency of Simultaneously Acquired Sentinel-2 and Landsat 8 Imagery on Plastic Covered Greenhouses

2020 ◽  
Vol 12 (12) ◽  
pp. 2015 ◽  
Author(s):  
Manuel Ángel Aguilar ◽  
Rafael Jiménez-Lao ◽  
Abderrahim Nemmaoui ◽  
Fernando José Aguilar ◽  
Dilek Koc-San ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Coefficient Of Determination ◽  
Landsat 8 ◽  
Surface Reflectance ◽  
Remote Sensing Techniques ◽  
Image Pairs ◽  
Study Sites ◽  
High Level ◽  
Sentinel 2

Remote sensing techniques based on medium resolution satellite imagery are being widely applied for mapping plastic covered greenhouses (PCG). This article aims at testing the spectral consistency of surface reflectance values of Sentinel-2 MSI (S2 L2A) and Landsat 8 OLI (L8 L2 and the pansharpened and atmospherically corrected product from L1T product; L8 PANSH) data in PCG areas located in Spain, Morocco, Italy and Turkey. The six corresponding bands of S2 and L8, together with the normalized difference vegetation index (NDVI), were generated through an OBIA approach for each PCG study site. The coefficient of determination (r2) and the root mean square error (RMSE) were computed in sixteen cloud-free simultaneously acquired image pairs from the four study sites to evaluate the coherence between the two sensors. It was found that the S2 and L8 correlation (r2 > 0.840, RMSE < 9.917%) was quite good in most bands and NDVI. However, the correlation of the two sensors fluctuated between study sites, showing occasional sun glint effects on PCG roofs related to the sensor orbit and sun position. Moreover, higher surface reflectance discrepancies between L8 L2 and L8 PANSH data, mainly in the visible bands, were always observed in areas with high-level aerosol values derived from the aerosol quality band included in the L8 L2 product (SR aerosol). In this way, the consistency between L8 PANSH and S2 L2A was improved mainly in high-level aerosol areas according to the SR aerosol band.

scholarly journals Sentinel-2 Based Temporal Detection of Agricultural Land Use Anomalies in Support of Common Agricultural Policy Monitoring

2018 ◽  
Vol 7 (10) ◽  
pp. 405 ◽  
Author(s):  
Urška Kanjir ◽  
Nataša Đurić ◽  
Tatjana Veljanovski
Keyword(s):  
Land Use ◽  
Time Series ◽  
Agricultural Policy ◽  
Common Agricultural Policy ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Agricultural Land ◽  
Agricultural Land Use ◽  
Land Use Control ◽  
Sentinel 2

The European Common Agricultural Policy (CAP) post-2020 timeframe reform will reshape the agriculture land use control procedures from a selected risk fields-based approach into an all-inclusive one. The reform fosters the use of Sentinel data with the objective of enabling greater transparency and comparability of CAP results in different Member States. In this paper, we investigate the analysis of a time series approach using Sentinel-2 images and the suitability of the BFAST (Breaks for Additive Season and Trend) Monitor method to detect changes that correspond to land use anomaly observations in the assessment of agricultural parcel management activities. We focus on identifying certain signs of ineligible (inconsistent) use in permanent meadows and crop fields in one growing season, and in particular those that can be associated with time-defined greenness (vegetation vigor). Depending on the requirements of the BFAST Monitor method and currently time-limited Sentinel-2 dataset for the reliable anomaly study, we introduce customized procedures to support and verify the BFAST Monitor anomaly detection results using the analysis of NDVI (Normalized Difference Vegetation Index) object-based temporal profiles and time-series standard deviation output, where geographical objects of interest are parcels of particular land use. The validation of land use candidate anomalies in view of land use ineligibilities was performed with the information on declared land annual use and field controls, as obtained in the framework of subsidy granting in Slovenia. The results confirm that the proposed combined approach proves efficient to deal with short time series and yields high accuracy rates in monitoring agricultural parcel greenness. As such it can already be introduced to help the process of agricultural land use control within certain CAP activities in the preparation and adaptation phase.

Evaluating the Persistence of Post-Wildfire Ash: A Multi-Platform Spatiotemporal Analysis

Fire ◽  
2021 ◽  
Vol 4 (4) ◽  
pp. 68
Author(s):  
Sarah A. Lewis ◽  
Peter R. Robichaud ◽  
Andrew T. Hudak ◽  
Eva K. Strand ◽  
Jan U. H. Eitel ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Field Measurements ◽  
Cost Effective ◽  
Spatiotemporal Analysis ◽  
Short Wave ◽  
Hydrologic Model ◽  
Landsat 8 ◽  
Data Volume ◽  
Sentinel 2

As wildland fires amplify in size in many regions in the western USA, land and water managers are increasingly concerned about the deleterious effects on drinking water supplies. Consequences of severe wildfires include disturbed soils and areas of thick ash cover, which raises the concern of the risk of water contamination via ash. The persistence of ash cover and depth were monitored for up to 90 days post-fire at nearly 100 plots distributed between two wildfires in Idaho and Washington, USA. Our goal was to determine the most ‘cost’ effective, operational method of mapping post-wildfire ash cover in terms of financial, data volume, time, and processing costs. Field measurements were coupled with multi-platform satellite and aerial imagery collected during the same time span. The image types spanned the spatial resolution of 30 m to sub-meter (Landsat-8, Sentinel-2, WorldView-2, and a drone), while the spectral resolution spanned visible through SWIR (short-wave infrared) bands, and they were all collected at various time scales. We that found several common vegetation and post-fire spectral indices were correlated with ash cover (r = 0.6–0.85); however, the blue normalized difference vegetation index (BNDVI) with monthly Sentinel-2 imagery was especially well-suited for monitoring the change in ash cover during its ephemeral period. A map of the ash cover can be used to estimate the ash load, which can then be used as an input into a hydrologic model predicting ash transport and fate, helping to ultimately improve our ability to predict impacts on downstream water resources.

scholarly journals Assessing Spatial Limits of Sentinel-2 Data on Arable Crops in the Context of Checks by Monitoring

2020 ◽  
Vol 12 (14) ◽  
pp. 2195 ◽  
Author(s):  
Blanka Vajsová ◽  
Dominique Fasbender ◽  
Csaba Wirnhardt ◽  
Slavko Lemajic ◽  
Wim Devos
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Policy Framework ◽  
Pixel Size ◽  
Control Approach ◽  
Continuous State ◽  
Ndvi Time Series ◽  
High Level ◽  
Sentinel 2

The availability of large amounts of Sentinel-2 data has been a trigger for its increasing exploitation in various types of applications. It is, therefore, of importance to understand the limits above which these data still guarantee a meaningful outcome. This paper proposes a new method to quantify and specify restrictions of the Sentinel-2 imagery in the context of checks by monitoring, a newly introduced control approach within the European Common Agriculture Policy framework. The method consists of a comparison of normalized difference vegetation index (NDVI) time series constructed from data of different spatial resolution to estimate the performance and limits of the coarser one. Using similarity assessment of Sentinel-2 (10 m pixel size) and PlanetScope (3 m pixel size) NDVI time series, it was estimated that for 10% out of 867 fields less than 0.5 ha in size, Sentinel-2 data did not provide reliable evidence of the activity or state of the agriculture field over a given timeframe. Statistical analysis revealed that the number of clean or full pixels and the proportion of pixels lost after an application of a 5-m (1/2 pixel) negative buffer are the geospatial parameters of the field that have the highest influence on the ability of the Sentinel-2 data to qualify the field’s state in time. We specified the following limiting criteria: at least 8 full pixels inside a border and less than 60% of pixels lost. It was concluded that compliance with the criteria still assures a high level of extracted information reliability. Our research proved the promising potential, which was higher than anticipated, of Sentinel-2 data for the continuous state assessment of small fields. The method could be applied to other sensors and indicators.

scholarly journals Detecting Cover Crop End-Of-Season Using VENµS and Sentinel-2 Satellite Imagery

2020 ◽  
Vol 12 (21) ◽  
pp. 3524
Author(s):  
Feng Gao ◽  
Martha C. Anderson ◽  
W. Dean Hively
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Cover Crops ◽  
Cover Crop ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Agricultural Research ◽  
Agricultural Landscapes ◽  
Absolute Difference ◽  
Sentinel 2

Cover crops are planted during the off-season to protect the soil and improve watershed management. The ability to map cover crop termination dates over agricultural landscapes is essential for quantifying conservation practice implementation, and enabling estimation of biomass accumulation during the active cover period. Remote sensing detection of end-of-season (termination) for cover crops has been limited by the lack of high spatial and temporal resolution observations and methods. In this paper, a new within-season termination (WIST) algorithm was developed to map cover crop termination dates using the Vegetation and Environment monitoring New Micro Satellite (VENµS) imagery (5 m, 2 days revisit). The WIST algorithm first detects the downward trend (senescent period) in the Normalized Difference Vegetation Index (NDVI) time-series and then refines the estimate to the two dates with the most rapid rate of decrease in NDVI during the senescent period. The WIST algorithm was assessed using farm operation records for experimental fields at the Beltsville Agricultural Research Center (BARC). The crop termination dates extracted from VENµS and Sentinel-2 time-series in 2019 and 2020 were compared to the recorded termination operation dates. The results show that the termination dates detected from the VENµS time-series (aggregated to 10 m) agree with the recorded harvest dates with a mean absolute difference of 2 days and uncertainty of 4 days. The operational Sentinel-2 time-series (10 m, 4–5 days revisit) also detected termination dates at BARC but had 7% missing and 10% false detections due to less frequent temporal observations. Near-real-time simulation using the VENµS time-series shows that the average lag times of termination detection are about 4 days for VENµS and 8 days for Sentinel-2, not including satellite data latency. The study demonstrates the potential for operational mapping of cover crop termination using high temporal and spatial resolution remote sensing data.

scholarly journals Sentinel-2 Satellite Imagery for Agronomic and Quality Variability Assessment of Pistachio (Pistacia vera L.)

2020 ◽  
Vol 12 (20) ◽  
pp. 8437
Author(s):  
Enrique Barajas ◽  
Sara Álvarez ◽  
Elena Fernández ◽  
Sergio Vélez ◽  
José Antonio Rubio ◽  
...  
Keyword(s):  
Satellite Imagery ◽  
Nutritional Quality ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Quality Parameters ◽  
Productive Capacity ◽  
Pistacia Vera ◽  
Phenological Stages ◽  
Organoleptic Characteristics ◽  
Sentinel 2

The objective of this work is to evaluate the agronomic, phenological, nutritional quality and organoleptic characteristics of pistachios (Pistacia vera L.) based on the NDVI (Normalized Difference Vegetation Index) calculated in the phenological stage of nut filling from Sentinel satellite imagery. Based on this index, three pistachio tree orchards were studied and classified into two levels of vigour: high and low. The results obtained have discriminated the production per tree, which is strongly related to yield. Regarding the nutritional quality parameters, significant differences were not observed between vigour levels, although the most vigorous trees have shown nuts with a higher percentage of fibre and protein. In terms of phenology, there have not been differences between trees of different vigour, only a slight advance of some phenological stages has been observed in several high-vigour trees. Triangular tests have been made successfully to discriminate the origin of the dry nut and the vigour of the trees. In conclusion, for a given nut quality within a given orchard, the NDVI is a good index to classify different areas according to productive capacity and can be useful to apply variable management, irrigation and fertilization according to vigour.

Sign in / Sign up

Export Citation Format

Share Document