The Effect of the Normalized Difference Vegetation Index to Landslide Susceptibility using Optical Imagery Sentinel 2 and Landsat 8

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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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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IDENTIFICATION OF DROUGHT EXTENT BASED ON VCI USING SENTINEL DATA: A CASE STUDY OF THE EASTERN OF IAŞI COUNTY

Present Environment and Sustainable Development ◽

10.15551/pesd2019132013 ◽

2019 ◽

Vol 13 (2) ◽

pp. 179-186

Author(s):

Paul Macarof ◽

Florian Statescu ◽

Cristian Iulian Birlica ◽

Paul Gherasim

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

European Space Agency ◽

Condition Index ◽

Landsat 8 ◽

Natural Phenomena ◽

Space Agency ◽

Vegetation Condition Index ◽

Sentinel 2

In this study was analyzed zones affected by drought using Vegetation Condition Index (VCI), that is based on Normalized Difference Vegetation Index (NDVI). This fact, drought, is one of the most wide -spread and least understood natural phenomena. In this paper was used remote sensing (RS) data, kindly provided by The European Space Agency (ESA), namely Sentinel-2 (S-2) Multispectral Instrument (MSI) and wellkonwn images Landsat 8 Operational Land Imager (OLI). The RS images was processed in SNAP and ArcMap. Study Area, was considered the eastern of Iasi county. The main purpose of paper was to investigating if Sentinel images can be used for VCI analysis.

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Comparative Analysis of Landsat-8, Sentinel-2, and GF-1 Data for Retrieving Soil Moisture over Wheat Farmlands

Remote Sensing ◽

10.3390/rs12172708 ◽

2020 ◽

Vol 12 (17) ◽

pp. 2708 ◽

Cited By ~ 1

Author(s):

Qi Wang ◽

Jiancheng Li ◽

Taoyong Jin ◽

Xin Chang ◽

Yongchao Zhu ◽

...

Keyword(s):

Soil Moisture ◽

Optical Sensors ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Cloud Model ◽

Vegetation Indices ◽

Optical Data ◽

Landsat 8 ◽

Different Sources ◽

Sentinel 2

Soil moisture is an important variable in ecological, hydrological, and meteorological studies. An effective method for improving the accuracy of soil moisture retrieval is the mutual supplementation of multi-source data. The sensor configuration and band settings of different optical sensors lead to differences in band reflectivity in the inter-data, further resulting in the differences between vegetation indices. The combination of synthetic aperture radar (SAR) data with multi-source optical data has been widely used for soil moisture retrieval. However, the influence of vegetation indices derived from different sources of optical data on retrieval accuracy has not been comparatively analyzed thus far. Therefore, the suitability of vegetation parameters derived from different sources of optical data for accurate soil moisture retrieval requires further investigation. In this study, vegetation indices derived from GF-1, Landsat-8, and Sentinel-2 were compared. Based on Sentinel-1 SAR and three optical data, combined with the water cloud model (WCM) and the advanced integral equation model (AIEM), the accuracy of soil moisture retrieval was investigated. The results indicate that, Sentinel-2 data were more sensitive to vegetation characteristics and had a stronger capability for vegetation signal detection. The ranking of normalized difference vegetation index (NDVI) values from the three sensors was as follows: the largest was in Sentinel-2, followed by Landsat-8, and the value of GF-1 was the smallest. The normalized difference water index (NDWI) value of Landsat-8 was larger than that of Sentinel-2. With reference to the relative components in the WCM model, the contribution of vegetation scattering exceeded that of soil scattering within a vegetation index range of approximately 0.55–0.6 in NDVI-based models and all ranges in NDWI1-based models. The threshold value of NDWI2 for calculating vegetation water content (VWC) was approximately an NDVI value of 0.4–0.55. In the soil moisture retrieval, Sentinel-2 data achieved higher accuracy than data from the other sources and thus was more suitable for the study for combination with SAR in soil moisture retrieval. Furthermore, compared with NDVI, higher accuracy of soil moisture could be retrieved by using NDWI1 (R2 = 0.623, RMSE = 4.73%). This study provides a reference for the selection of optical data for combination with SAR in soil moisture retrieval.

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Change Detection and Impact of Climate Changes to Iraqi Southern Marshes Using Landsat 2 MSS, Landsat 8 OLI and Sentinel 2 MSI Data and GIS Applications

Applied Sciences ◽

10.3390/app9102016 ◽

2019 ◽

Vol 9 (10) ◽

pp. 2016 ◽

Cited By ~ 1

Author(s):

Bassim Mohammed Hashim ◽

Maitham Abdullah Sultan ◽

Mazin Najem Attyia ◽

Ali A. Al Maliki ◽

Nadhir Al-Ansari

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Maximum Temperature ◽

Landsat 8 ◽

Landsat 8 Oli ◽

Large Area ◽

Gis Applications ◽

Average Maximum ◽

Water Cover ◽

Sentinel 2

Marshes represent a unique ecosystem covering a large area of southern Iraq. In a major environmental disaster, the marshes of Iraq were drained, especially during the 1990s. Since then, droughts and the decrease in water imports from the Tigris and Euphrates rivers from Turkey and Iran have prevented them from regaining their former extent. The aim of this research is to extract the values of the normalized difference vegetation index (NDVI) for the period 1977–2017 from Landsat 2 MSS (multispectral scanner), Landsat 8 OLI (operational land imager) and Sentinel 2 MSI (multi-spectral imaging mission) satellite images and use supervised classification to quantify land and water cover change. The results from the two satellites (Landsat 2 and Landsat 8) are compared with Sentinel 2 to determine the best tool for detecting changes in land and water cover. We also assess the potential impacts of climate change through the study of the annual average maximum temperature and precipitation in different areas in the marshes for the period 1981–2016. The NDVI analysis and image classification showed the degradation of vegetation and water bodies in the marshes, as vast areas of natural vegetation and agricultural lands disappeared and were replaced with barren areas. The marshes were influenced by climatic change, including rising temperature and the diminishing amount of precipitation during 1981–2016.

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Combined Use of Multi-Temporal Landsat-8 and Sentinel-2 Images for Wheat Yield Estimates at the Intra-Plot Spatial Scale

Agronomy ◽

10.3390/agronomy10030327 ◽

2020 ◽

Vol 10 (3) ◽

pp. 327 ◽

Cited By ~ 4

Author(s):

Remy Fieuzal ◽

Vincent Bustillo ◽

David Collado ◽

Gerard Dedieu

Keyword(s):

Relative Error ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Wheat Yield ◽

Coefficient Of Determination ◽

Landsat 8 ◽

Optical Images ◽

Multi Temporal ◽

Input Variables ◽

Sentinel 2

The objective of this study is to address the capabilities of multi-temporal optical images to estimate the fine-scale yield variability of wheat, over a study site located in southwestern France. The methodology is based on the Landsat-8 and Sentinel-2 satellite images acquired after the sowing and before the harvest of the crop throughout four successive agricultural seasons, the reflectance constituting the input variables of a statistical algorithm (random forest). The best performances are obtained when the Normalized Difference Vegetation Index (NDVI) is combined with the yield maps collected during the crop rotation, the agricultural season 2014 showing the lower level of performances with a coefficient of determination (R2) of 0.44 and a root mean square error (RMSE) of 8.13 quintals by hectare (q.h−1) (corresponding to a relative error of 12.9%), the three other years being associated with values of R2 close or upper to 0.60 and RMSE lower than 7 q.h−1 (corresponding to a relative error inferior to 11.3%). Moreover, the proposed approach allows estimating the crop yield throughout the agricultural season, by using the successive images acquired from the sowing to the harvest. In such cases, early and accurate yield estimates are obtained three months before the end of the crop cycle. At this phenological stage, only a slight decrease in performance is observed compared to the statistic obtained just before the harvest.

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Comparação de imagens multiespectrais utilizando Satélites e VANT para a análise de mudanças estruturais em área de Floresta Seca

Revista Brasileira de Geografia Física ◽

10.26848/rbgf.v14.5.p2510-2522 ◽

2021 ◽

pp. 2510-2522

Author(s):

Cloves Santos ◽

Magna Moura ◽

Josicleda Galvincio ◽

Herica Carvalho ◽

Rodrigo Miranda ◽

...

Keyword(s):

High Resolution ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Structural Characteristics ◽

Landsat 8 ◽

Green Vegetation ◽

Detailed Assessment ◽

Low Altitude ◽

Uav Images ◽

Sentinel 2

Remote sensing is a very important tool in the acquisition of information that allows the monitoring of structural characteristics and changes in vegetation in biomes, and with the use of spectral indices of vegetation, it is possible to analyze its dynamics over time. This study aims to analyze the structure of vegetation cover in an area of the Caatinga Biome, comparing multispectral images acquired by satellite with different resolutions and low altitude unmanned aerial vehicle (UAV) platforms with high resolution cameras. Automated flights were carried out in November and December 2019 over the study area and the images were processed to generate orthomosaics. Landsat-8 and Sentinel-2 satellite images were acquired free of charge for comparison purposes with the UAV. The vigor of green vegetation was analyzed through the calculation of the Normalized Difference Vegetation Index (NDVI) and verified through the correlation between high resolution and low altitude products with satellites. Both products from satellites proved to be effective and good indicators of vegetation vigor, with emphasis on Sentinel-2 images, which obtained a better correlation with aerial UAV images reaching (R = 0.7) compared to Landsat-8 (R = 0.6). Satellite products showed good indicators for monitoring the structural characteristics of the Caatinga, however, they are not indicated for assessments of areas with a greater predominance of soil, water or other targets, as they can affect the NDVI values and make a more detailed assessment impossible. of the areas.

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Estimation of Wheat Yields at the Intra-Plot Scale by Combining Multi-Temporal Landsat-8 and Sentinel-2 Images

Proceedings ◽

10.3390/iecg2019-06220 ◽

2019 ◽

Vol 24 (1) ◽

pp. 14

Author(s):

Remy Fieuzal ◽

Vincent Bustillo ◽

David Collado ◽

Gerard Dedieu

Keyword(s):

Relative Error ◽

Satellite Images ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Yield Variability ◽

Landsat 8 ◽

Optical Images ◽

Multi Temporal ◽

Input Variables ◽

Sentinel 2

The objective of this study is to address the capabilities of multi-temporal optical images to estimate the fine-scale yield variability of wheat over a study site located in southwestern France. The methodology is based on the use of Landsat-8 and Sentinel-2 satellite images acquired after the sowing and before the harvest of the crop throughout four successive agricultural seasons, the reflectance constituting the input variables of a statistical algorithm (random forest). The best performances are obtained when the NDVI (Normalized Difference Vegetation Index) is combined with the previous yield maps, the agricultural season 2014 showing the lower level of performances with a R² of 0.44 and a RMSE (Root Mean Square Error) of 8.13 q.h−1 (corresponding to a relative error of 12.9%), the three other years being associated with values of R² close or upper of 0.60 and RMSE lower than 7 q.h−1 (corresponding to a relative error inferior to 11.3%).

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Design of a Local Nested Grid for the Optimal Combined Use of Landsat 8 and Sentinel 2 Data

Remote Sensing ◽

10.3390/rs13081546 ◽

2021 ◽

Vol 13 (8) ◽

pp. 1546

Author(s):

David Hernández-López ◽

Laura Piedelobo ◽

Miguel A. Moreno ◽

Amal Chakhar ◽

Damián Ortega-Terol ◽

...

Keyword(s):

Remote Sensing ◽

River Basin ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Remote Sensing Data ◽

Landsat 8 ◽

Full Potential ◽

Combined Use ◽

Nested Grid ◽

Sentinel 2

Earth Observation (EO) imagery is difficult to find and access for the intermediate user, requiring advanced skills and tools to transform it into useful information. Currently, remote sensing data is increasingly freely and openly available from different satellite platforms. However, the variety of images in terms of different types of sensors, spatial and spectral resolutions generates limitations due to the heterogeneity and complexity of the data, making it difficult to exploit the full potential of satellite imagery. Addressing this issue requires new approaches to organize, manage, and analyse remote-sensing imagery. This paper focuses on the growing trend based on satellite EO and the analysis-ready data (ARD) to integrate two public optical satellite missions: Landsat 8 (L8) and Sentinel 2 (S2). This paper proposes a new way to combine S2 and L8 imagery based on a Local Nested Grid (LNG). The LNG designed plays a key role in the development of new products within the European EO downstream sector, which must incorporate assimilation techniques and interoperability best practices, automatization, systemization, and integrated web-based services that will potentially lead to pre-operational downstream services. The approach was tested in the Duero river basin (78,859 km2) and in the groundwater Mancha Oriental (7279 km2) in the Jucar river basin, Spain. In addition, a viewer based on Geoserver was prepared for visualizing the LNG of S2 and L8, and the Normalized Difference Vegetation Index (NDVI) values in points. Thanks to the LNG presented in this paper, the processing, storage, and publication tasks are optimal for the combined use of images from two different satellite sensors when the relationship between spatial resolutions is an integer (3 in the case of L8 and S2).

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Change Detection between Landsat 8 images and Sentinel-2 images

Iraqi Journal of Science ◽

10.24996/ijs.2019.60.8.24 ◽

2019 ◽

pp. 1868-1876

Author(s):

Nawfal S. Abd-Alwahab ◽

Nawal K. Ghazal

Keyword(s):

Change Detection ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Wheat Crop ◽

Landsat 8 ◽

Agriculture Land ◽

Time Period ◽

Winter Crops ◽

The Difference ◽

Sentinel 2

The technology of change detection is a technique by which changes are verified in a certain time period. Remote sensing images are used to detect changes in agriculture land for the selected study area located south of Baghdad governorate in Agricultural Division of AL-Rasheed district because this method is very effective for assessing change compared to other traditional scanning techniques. In this research two remotely sensed images for the study area were taken by Landsat 8 and Sentinel-2, the difference between them is one month to monitor the change in the winter crops, especially the wheat crop, where the agriculture began for the wheat crop there in the Agricultural Division of AL-Rasheed district at 15/11/2018. The first preprocessing procedure was the extraction of the NDVI (Normalized Difference Vegetation Index) values for the two scenes of Landsat 8 and the two scenes of Sentinel-2B and then using the change detection between them to compare the changes in agriculture land. Also, change detection was implemented between NIR bands because they are most severely affected by biomass or the amount of available chlorophyll-containing in plant structures. The results of the change detection for Sentinel-2B were more accurate than for the Landsat 8 as demonstrated by field visits for the study area, where the changes in the distribution of vegetal cover (wheat and other winter crops) were clear and accurate in the image of Sentinel-2B, as opposed to Landsat's 8 image, where the variation in vegetation cover was not accurate, especially for the change detection between NIR bands.

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