The potentiality of Sentinel-2 to assess the effect of fire events on Mediterranean mountain vegetation

Wildfires are currently one of the most important environmental problems, as they cause disturbance in ecosystems generating environmental, economic and social costs. The Sentinel-2 from Copernicus Program (Sentinel satellites) offers a great tool for post-fire monitoring. The main objective of this study is to evaluate the potential of Sentinel-2 in a peculiar mountainous landscape by measuring and identifying the burned areas and monitor the short-term response of the vegetation in different ‘burn severity’ classes. A Sentinel-2 dataset was created, and pre-processing operations were performed. Relativized Burn Ratio (RBR) was calculated to identify ‘burn scar’ and discriminate the ‘burn severity’ classes. A two-year monitoring was carried out with areas identified based on different severity classes, using Normalized Difference Vegetation Index (NDVI) to investigate the short-term vegetation dynamics of the burned habitats; habitats refer to Annex I of the European Directive 92/43/EEC. The study area is located in ‘Campo Imperatore’ within the Gran Sasso – Monti della Laga National Park (central Italy). The first important result was the identification and quantification of the area affected by fire. The RBR allowed us to identify even the less damaged habitats with high accuracy. The survey highlighted the importance of these Open-source tools for qualitative and quantitative evaluation of fires and the short-term assessment of vegetation recovery dynamics. The information gathered by this type of monitoring can be used by decision-makers both for emergency management and for possible environmental restoration of the burned areas.

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Cartografía de la afección y recuperación vegetal del incendio de Las Peñuelas en Moguer (Huelva) con imágenes satelitales. Año 2017

Revista de Teledetección ◽

10.4995/raet.2020.13082 ◽

2020 ◽

pp. 79

Author(s):

J.J. Vales ◽

I. Pino ◽

L. Granado ◽

R. Prieto ◽

E. Méndez ◽

...

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Fire Severity ◽

Burned Areas ◽

Area Of Interest ◽

Fire Environment ◽

Recovery Strategies ◽

Aerial Vehicle ◽

Index Index ◽

Sentinel 2

<p>Deep knowledge of the regeneration processes after a forest fire is key to addressing their adverse environmental impacts, these are especially evident in the vegetation. In the post-fire environment context, the fire severity constitutes a critical variable that affects the ecosystem response in terms of vegetation recovery and hydrogeomorphological dynamics after the fire. Therefore, the severity accurate assessment is essential for the burned areas management because of it allows the identification of priority areas and, therefore, it helps to carry out recovery strategies and measures. The area of interest is located in the natural place of Las Peñuelas (Huelva), where a large fire took place on June 24, 2017 that affected almost 10 000 ha. The methodology was based on the calculation of the RBR (Relativized Burn Ratio) spectral index to estimate the severity of the fire, and the NDVI (Normalized Difference Vegetation Index) index to evaluate the recovery of vegetal vigor. For the work, images from the Sentinel-2 and Pleiades satellites, images acquired by UAV (Unmanned Aerial Vehicle) and field samplings were used. The result was a cartography showing the levels of recovery or degradation of the affected vegetation.</p>

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A Statistical and Spatial Analysis of Portuguese Forest Fires in Summer 2016 Considering Landsat 8 and Sentinel 2A Data

Environments ◽

10.3390/environments6030036 ◽

2019 ◽

Vol 6 (3) ◽

pp. 36 ◽

Cited By ~ 9

Author(s):

Ana Teodoro ◽

Ana Amaral

Keyword(s):

Forest Fires ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Burn Severity ◽

Landsat 8 ◽

Fire Event ◽

Burned Areas ◽

Area Burned ◽

Sentinel 2A ◽

Normalized Burn Ratio

Forest areas in Portugal are often affected by fires. The objective of this work was to analyze the most fire-affected areas in Portugal in the summer of 2016 for two municipalities considering data from Landsat 8 OLI and Sentinel 2A MSI (prefire and postfire data). Different remote sensed data-derived indices, such as Normalized Difference Vegetation Index (NDVI) and Normalized Burn Ratio (NBR), could be used to identify burnt areas and estimate the burn severity. In this work, NDVI was used to evaluate the area burned, and NBR was used to estimate the burn severity. The results showed that the NDVI decreased considerably after the fire event (2017 images), indicating a substantial decrease in the photosynthesis activity in these areas. The results also indicate that the NDVI differences (dNDVI) assumes the highest values in the burned areas. The results achieved for both sensors regarding the area burned presented differences from the field data no higher than 13.3% (for Sentinel 2A, less than 7.8%). We conclude that the area burned estimated using the Sentinel 2A data is more accurate, which can be justified by the higher spatial resolution of this data.

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

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10040251 ◽

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.

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Urban Fire Severity and Vegetation Dynamics in Southern California

Remote Sensing ◽

10.3390/rs13010019 ◽

2020 ◽

Vol 13 (1) ◽

pp. 19

Author(s):

Lauren E. H. Mathews ◽

Alicia M. Kinoshita

Keyword(s):

Vegetation Cover ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Current Knowledge ◽

Fire Severity ◽

Satellite Image ◽

Burn Severity ◽

Native Vegetation ◽

Riparian Areas ◽

The Impact

A combination of satellite image indices and in-field observations was used to investigate the impact of fuel conditions, fire behavior, and vegetation regrowth patterns, altered by invasive riparian vegetation. Satellite image metrics, differenced normalized burn severity (dNBR) and differenced normalized difference vegetation index (dNDVI), were approximated for non-native, riparian, or upland vegetation for traditional timeframes (0-, 1-, and 3-years) after eleven urban fires across a spectrum of invasive vegetation cover. Larger burn severity and loss of green canopy (NDVI) was detected for riparian areas compared to the uplands. The presence of invasive vegetation affected the distribution of burn severity and canopy loss detected within each fire. Fires with native vegetation cover had a higher severity and resulted in larger immediate loss of canopy than fires with substantial amounts of non-native vegetation. The lower burn severity observed 1–3 years after the fires with non-native vegetation suggests a rapid regrowth of non-native grasses, resulting in a smaller measured canopy loss relative to native vegetation immediately after fire. This observed fire pattern favors the life cycle and perpetuation of many opportunistic grasses within urban riparian areas. This research builds upon our current knowledge of wildfire recovery processes and highlights the unique challenges of remotely assessing vegetation biophysical status within urban Mediterranean riverine systems.

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Modeling of Durum Wheat Yield Based on Sentinel-2 Imagery

Agronomy ◽

10.3390/agronomy11081486 ◽

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.

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

Remote Sensing ◽

10.3390/rs13050956 ◽

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.

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

Remote Sensing ◽

10.3390/rs12122015 ◽

2020 ◽

Vol 12 (12) ◽

pp. 2015 ◽

Cited By ~ 2

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.

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

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi7100405 ◽

2018 ◽

Vol 7 (10) ◽

pp. 405 ◽

Cited By ~ 12

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.

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

Fire ◽

10.3390/fire4040068 ◽

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.

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

Remote Sensing ◽

10.3390/rs12142195 ◽

2020 ◽

Vol 12 (14) ◽

pp. 2195 ◽

Cited By ~ 4

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.

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