scholarly journals Contribution of the new satellites (Sentinel-1, Sentinel-2 and SPOT-6) to the coastal vegetation monitoring in the Pays de Brest (France)

2017 ◽  
Author(s):  
Halima Talab Ou Ali ◽  
Simona Niculescu ◽  
Christophe Bougault ◽  
Vanessa Sellin
Keyword(s):  
Coastal Vegetation ◽  
Vegetation Monitoring ◽  
Sentinel 2

A new approach for biocrust and vegetation monitoring in drylands using multi-temporal Sentinel-2 images

2019 ◽  
Vol 43 (4) ◽  
pp. 496-520 ◽  
Author(s):  
Cinzia Panigada ◽  
Giulia Tagliabue ◽  
Eli Zaady ◽  
Offer Rozenstein ◽  
Roberto Garzonio ◽  
...  
Keyword(s):  
Vascular Plant ◽  
Temporal Distribution ◽  
Soil Surface ◽  
Absorption Feature ◽  
Arid Environments ◽  
Vegetation Monitoring ◽  
Rainfall Gradient ◽  
The North ◽  
Multi Temporal ◽  
Sentinel 2

Drylands, one of the planet’s largest terrestrial biomes, are suggested to be greatly threatened by climate change. Drylands are usually sparsely vegetated, and biological soil crusts (biocrusts) – that is, soil surface communities of cyanobacteria, mosses and/or lichens – can cover up to 70% of dryland cover. As they control key ecosystem processes, monitoring their spatial and temporal distribution can provide highly valuable information. In this study, we examine the potential of European Space Agency’s (ESA) Sentinel-2 (S2) data to characterize the spatial and temporal development of biocrust and vascular plant greening along a rainfall gradient of the Negev Desert (Israel). First, the chlorophyll a absorption feature in the red region (CRred) was identified as the index mostly sensitive to changes in biocrust greening but minimally affected by changes in soil moisture. This index was then computed on the S2 images and enabled monitoring the phenological dynamics of different dryland vegetation components from August 2015 to August 2017. The analysis of multi-temporal S2 images allowed us to successfully track the biocrust greening within 15 days from the first seasonal rain events in the north of Negev, and to identify the maximum development of annual vascular plants and greening of perennial ones. These results show potential for monitoring arid and semi-arid environments using the newly available S2 images, allowing new insights into dryland vegetation dynamics.

An empirical cross sensor model for high-resolution multispectral imagery

2020 ◽  
Author(s):  
Manivasagam Vellalapalayam Subramanian ◽  
Gregoriy Kaplan ◽  
Offer Rozenstein
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
Transformation Model ◽  
Sensor Data ◽  
Absolute Difference ◽  
Landsat 8 ◽  
Vegetation Monitoring ◽  
Spectral Angle Mapper ◽  
Statistical Measures ◽  
Sentinel 2

<p>The availability of public-domain high-resolution satellite imagery such as Sentinel-2 and Landsat-8 has increased earth observation (EO) studies across the globe. Empirically combining different EO sensor data into a single dataset increases the temporal coverage, which is useful for land-cover monitoring. In this study, a transformation model was developed for Sentinel-2 and Vegetation and Environmental New micro Spacecraft (VENμS) imagery over Israel. Both sensors offer high spatio-temporal resolution imagery, i.e., VENμS has a 10m spatial resolution with a two-day revisit period, and Sentinel-2 has a 10-20 m spatial resolution with a five-day revisit period. Near-simultaneously acquired imagery was employed for the transformation model development. The model coefficients were derived for the overlapping spectral regions of both sensors. Further, the transformation model performance was tested using various statistical measures, namely, orthogonal distance regression (ODR), spectral angle mapper (SAM), and mean absolute difference (MAD). The validation results highlighted that MAD values were reduced between Sentinel-2 and transformed VENμS reflectance. Similarly, the ODR slope values became closer to one, and the overall spectral similarity increased as demonstrated by a decrease in SAM values. This transformation function creates a unified reflectance dataset in the form of a dense time-series of observation, especially useful for vegetation monitoring.</p>

scholarly journals Contribution of Sentinel-2 data for applications in vegetation monitoring

ACTA IMEKO ◽  
2016 ◽  
Vol 5 (2) ◽  
pp. 44 ◽  
Author(s):  
Pia Addabbo ◽  
Mariano Focareta ◽  
Salvo Marcuccio ◽  
Claudio Votto ◽  
Silvia Liberata Ullo
Keyword(s):  
Added Value ◽  
Landsat 8 ◽  
Vegetation Monitoring ◽  
Multispectral Data ◽  
Monitoring Applications ◽  
Green Areas ◽  
Land Surfaces ◽  
Project Data ◽  
Different Sources ◽  
Sentinel 2

<p class="Abstract"><span lang="EN-US">With the entry into operation of the Sentinel-2 mission in June 2015, a new land monitoring costellation of twin satellites has been added to Copernicus project from ESA and new insights have been derived through the combination of Sentinel-2 data with other optical/multispectral data, and with other data from satellites belonging to the same Copernicus  project.  To this end, the objective of this paper has been to present new added-value tools first through the integration of different satellite platforms: data from NASA Landsat-8 and ESA Sentinel-1 have been used and combined, and furthermore through the comparison of satellite data all from the same Copernicus project: data from Sentinel-1 and Sentinel-2 have been jointly processed and compared. Although data from optical/multispectral sensors, as those of Landsat-8 and Sentinel-2, and data from SAR on board of Sentinel-1,  are very different,  their combination provides useful and interesting results. The integration and combination of these data can find useful application in many fields from oceans to waterways, from land surfaces to fossil deposits, from vegetation to forest areas. In this works authors have focused their interest in green areas and vegetation monitoring applications, by choosing as case of interest the Royal Palace of Caserta and its gardens.  The idea has started from the increasing interest in monitoring  the cultural heritage monuments and in particular  the surrounding vegetation with the green areas and the parks inside. Satellite images can put into evidence boundaries modifications, the vegetation state, their possible degradation, and other phenomena such as changes in the territories due both to natural and to anthropogenic causes. Data combination from different sources as above specified gives a good number of indexes very useful to analyze the vegetation state and its health in a very deep way. Many of these indexes have been calculated and discussed for investigation.</span></p>

Fuel Break Vegetation Monitoring with Sentinel-2 NDVI Robust to Phenology and Environmental Conditions

2021 ◽  
Author(s):  
Joao E. Pereira-Pires ◽  
Valentine Aubard ◽  
Rita A. Ribeiro ◽  
Jose M. Fonseca ◽  
Joao M. N. Silva ◽  
...  
Keyword(s):  
Environmental Conditions ◽  
Vegetation Monitoring ◽  
Sentinel 2

scholarly journals Multi-Temporal Sentinel-2 Data in Classification of Mountain Vegetation

2020 ◽  
Vol 12 (17) ◽  
pp. 2696 ◽  
Author(s):  
Martyna Wakulińska ◽  
Adriana Marcinkowska-Ochtyra
Keyword(s):  
National Parks ◽  
Vegetation Indices ◽  
European Space Agency ◽  
Principal Component ◽  
Support Vector ◽  
Electromagnetic Spectrum ◽  
Vegetation Monitoring ◽  
Multi Temporal ◽  
Mountain Vegetation ◽  
Sentinel 2

The electromagnetic spectrum registered via satellite remote sensing methods became a popular data source that can enrich traditional methods of vegetation monitoring. The European Space Agency Sentinel-2 mission, thanks to its spatial (10–20 m) and spectral resolution (12 spectral bands registered in visible-, near-, and mid-infrared spectrum) and primarily its short revisit time (5 days), helps to provide reliable and accurate material for the identification of mountain vegetation. Using the support vector machines (SVM) algorithm and reference data (botanical map of non-forest vegetation, field survey data, and high spatial resolution images) it was possible to classify eight vegetation types of Giant Mountains: bogs and fens, deciduous shrub vegetation, forests, grasslands, heathlands, subalpine tall forbs, subalpine dwarf pine scrubs, and rock and scree vegetation. Additional variables such as principal component analysis (PCA) bands and selected vegetation indices were included in the best classified dataset. The results of the iterative classification, repeated 100 times, were assessed as approximately 80% median overall accuracy (OA) based on multi-temporal datasets composed of images acquired through the vegetation growing season (from late spring to early autumn 2018), better than using a single-date scene (70%–72% OA). Additional variables did not significantly improve the results, showing the importance of spectral and temporal information themselves. Our study confirms the possibility of fully available data for the identification of mountain vegetation for management purposes and protection within national parks.

scholarly journals Developing Transformation Functions for VENμS and Sentinel-2 Surface Reflectance over Israel

2019 ◽  
Vol 11 (14) ◽  
pp. 1710 ◽  
Author(s):  
V.S. Manivasagam ◽  
Gregoriy Kaplan ◽  
Offer Rozenstein
Keyword(s):  
Time Series ◽  
Near Infrared ◽  
Transformation Model ◽  
Absolute Difference ◽  
Surface Reflectance ◽  
Vegetation Monitoring ◽  
Blue Band ◽  
Spectral Angle Mapper ◽  
Transformation Functions ◽  
Sentinel 2

Vegetation and Environmental New micro Spacecraft (VENμS) and Sentinel-2 are both ongoing earth observation missions that provide high-resolution multispectral imagery at 10 m (VENμS) and 10–20 m (Sentinel-2), at relatively high revisit frequencies (two days for VENμS and five days for Sentinel-2). Sentinel-2 provides global coverage, whereas VENμS covers selected regions, including parts of Israel. To facilitate the combination of these sensors into a unified time-series, a transformation model between them was developed using imagery from the region of interest. For this purpose, same-day acquisitions from both sensor types covering the surface reflectance over Israel, between April 2018 and November 2018, were used in this study. Transformation coefficients from VENμS to Sentinel-2 surface reflectance were produced for their overlapping spectral bands (i.e., visible, red-edge and near-infrared). The performance of these spectral transformation functions was assessed using several methods, including orthogonal distance regression (ODR), the mean absolute difference (MAD), and spectral angle mapper (SAM). Post-transformation, the value of the ODR slopes were close to unity for the transformed VENμS reflectance with Sentinel-2 reflectance, which indicates near-identity of the two datasets following the removal of systemic bias. In addition, the transformation outputs showed better spectral similarity compared to the original images, as indicated by the decrease in SAM from 0.093 to 0.071. Similarly, the MAD was reduced post-transformation in all bands (e.g., the blue band MAD decreased from 0.0238 to 0.0186, and in the NIR it decreased from 0.0491 to 0.0386). Thus, the model helps to combine the images from Sentinel-2 and VENμS into one time-series that facilitates continuous, temporally dense vegetation monitoring.

scholarly journals Crop Nitrogen Retrieval Methods for Simulated Sentinel-2 Data Using In-Field Spectrometer Data

2021 ◽  
Vol 13 (12) ◽  
pp. 2404
Author(s):  
Gregor Perich ◽  
Helge Aasen ◽  
Jochem Verrelst ◽  
Francesco Argento ◽  
Achim Walter ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Gaussian Processes ◽  
N Uptake ◽  
Prediction Performance ◽  
Importance Measure ◽  
Analysis Tool ◽  
Vegetation Monitoring ◽  
Area Index ◽  
Spectrometer Data ◽  
Sentinel 2

Nitrogen (N) is one of the key nutrients supplied in agricultural production worldwide. Over-fertilization can have negative influences on the field and the regional level (e.g., agro-ecosystems). Remote sensing of the plant N of field crops presents a valuable tool for the monitoring of N flows in agro-ecosystems. Available data for validation of satellite-based remote sensing of N is scarce. Therefore, in this study, field spectrometer measurements were used to simulate data of the Sentinel-2 (S2) satellites developed for vegetation monitoring by the ESA. The prediction performance of normalized ratio indices (NRIs), random forest regression (RFR) and Gaussian processes regression (GPR) for plant-N-related traits was assessed on a diverse real-world dataset including multiple crops, field sites and years. The plant N traits included the mass-based N measure, N concentration in the biomass (Nconc), and an area-based N measure approximating the plant N uptake (NUP). Spectral indices such as normalized ratio indices (NRIs) performed well, but the RFR and GPR methods outperformed the NRIs. Key spectral bands for each trait were identified using the RFR variable importance measure and the Gaussian processes regression band analysis tool (GPR-BAT), highlighting the importance of the short-wave infrared (SWIR) region for estimation of plant Nconc—and to a lesser extent the NUP. The red edge (RE) region was also important. The GPR-BAT showed that five bands were sufficient for plant N trait and leaf area index (LAI) estimation and that a surplus of bands effectively reduced prediction performance. A global sensitivity analysis (GSA) was performed on all traits simultaneously, showing the dominance of the LAI in the mixed remote sensing signal. To delineate the plant-N-related traits from this signal, regional and/or national data collection campaigns producing large crop spectral libraries (CSL) are needed. An improved database will likely enable the mapping of N at the agro-ecosystem level or for use in precision farming by farmers in the future.

scholarly journals EXPLOITATION OF SENTINEL-2 IMAGES FOR LONG-TERM VEGETATION MONITORING AT A FORMER ORE PROCESSING SITE

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 1533-1537
Author(s):  
S. Fabre ◽  
A. Elger ◽  
T. Riviere
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Environmental Changes ◽  
Biomass Accumulation ◽  
Growth Patterns ◽  
Absolute Difference ◽  
Vegetation Monitoring ◽  
Ore Processing ◽  
Processing Site ◽  
Sentinel 2

Abstract. Excess metals in the soil or in plant tissues tend to have negative effects on plant health, growth, and biomass accumulation. The search for stressed or unusual growth patterns in cover vegetation has been enhanced by the use of vegetation index in the context of excessive exposure to heavy metals in the soil. This study aims to improve the monitoring of phyto-stabilized and natural vegetation of an ore processing site for several years after its closure by using multiple Sentinel-2 images. The time series is made up of 13 images, one image per season for four years. NDVI (Normalized Difference Vegetation Index), the most widely known and used vegetation index in the scientific literature, is used in combination with other spectral indexes identifying built-up areas and bare soils in order to enhance vegetation. A change detection technique based on absolute difference of vegetation maps is applied to detect abrupt changes related to meteorological conditions and significant environmental changes.

scholarly journals SAGA GIS for Computing Multispectral Vegetation Indices by Landsat TM for Mapping Vegetation Greenness

2021 ◽  
Vol 70 (1-2) ◽  
pp. 67-75
Author(s):  
Polina Lemenkova
Keyword(s):  
Vegetation Indices ◽  
Landsat Tm ◽  
Coastal Vegetation ◽  
Atmospheric Effects ◽  
Vegetation Monitoring ◽  
Vegetation Greenness ◽  
Landsat Tm Image ◽  
Harsh Climate ◽  
Water Values ◽  
River Valleys

Summary The study presents a comparative analysis of eight Vegetation Indices (VIs) used to examine vegetation greenness over the northern coasts of Iceland. The geographical extent of the study area is set by the coordinates of the two fjords, Eyjafjörður and Skagafjörður, notable for their agricultural significance. Vegetation in Iceland is fragile due to the harsh climate, climate change, overgrazing and volcanic activity, which increase soil erosion. The study was conducted on a Landsat TM image using SAGA GIS as a technical tool for raster bands calculations. The NDVI dataset shows a range from -0.56 to 0.24, with 0 indicating ‘no vegetation’, and negative values – ‘other surfaces’ (e.g. rocks, open terrain). The DVI, compared to the NDVI, shows statistically non-normalized values ranging from -112 to 0, with extreme negative values while the coastal vegetation areas are badly distinguished from the water areas. The NRVI shows an extent from -0.24 to 0.48 with higher values for vegetation. The NRVI reduces topographic, solar and atmospheric effects and creates a normal data distribution. RVI shows a range in a dataset from 0.2 to 3.2 with vegetation in the river valleys clearly visible and depicted, while the water areas have values 0.8 to 1.0. The CTVI shows corrected TVI, in a data range -0.10 to 1.10, as the dataset of NDVI were negative. The TVI dataset ranges from 0.44 to 0.80 with the ice-covered areas and glaciers distinguishable and water values within a range from 0.60 to 0.64 and the vegetation from 0.60 to 0.44. The TTVI dataset ranges from 0.40 to 0.80 performing similarly to the TVI, but more refined with vegetation values 0.64 to 0.68. SAVI dataset ranges from -0.80 to 0.30 with minimized effects of soil on the vegetation through a constant soil adjustment factor added into the NDVI formula. The paper presents a comparison of eight VIs for Arctic vegetation monitoring. The overall behavior of SAGA GIS in calculation and mapping of the VIs is effective in terms of their use for vegetation mapping of the region.

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