Top PDF Contribution of Sentinel-2 data for applications in vegetation monitoring

Contribution of Sentinel-2 data for applications in vegetation monitoring

Contribution of Sentinel-2 data for applications in vegetation monitoring

oceanography (i.e. waves, wind, ship detection), man-caused disasters management (oil spills and monitoring of patches), to name a few [3]. In very recent years scientists have realized how remote sensing can find a new application by providing accurate, inexpensive, and timely information related to artistic heritage, either natural (parks, landscapes, etc.) or cultural (monuments, archaeological sites, and so on) [4]. The action of man, pollution, corrosion due to rain, cold, harsh weather conditions, solar radiation and thermal stress, can damage the artistic heritage. In addition, operations like excavations and corings have appeared to be risky, expensive and invasive techniques. On the other side, remote sensing presents many advantages: a global vision of the artistic and cultural sites, included the surrounding area; a periodic monitoring of the events, with quite fast intervention in case anomalies or dangers are detected; the identification of certain sites in the world, that present characteristics so special to be classified and associated, for instance, as part of World Heritage; besides the possibility to locate buried, not yet excavated sites. Another consideration is that environmental changes and their actions are very slow and the resulting damages appear only after a very long time. Remote sensing can help also in this sense, because of its ability to discriminate even small changes.
Show more

11 Read more

Sentinel-2/MSI applications for European Union Water Framework Directive reporting purposes

Sentinel-2/MSI applications for European Union Water Framework Directive reporting purposes

environmental monitoring. These possibilities advance the research based on remote sensing data, which is very valuable for estimating water quality (Klein et al. 2017). Satellite images of waterbodies provide an opportunity to derive some of the important parameters of water quality for fulfilling WFD requirements such as chl-a, phytoplankton biomass and transparency (Alikas et al. 2015). Chl-a is a pigment of phytoplankton, that indicates the trophic status of water. Through the photosynthesis, the phytoplankton convert CO 2 and H 2 O into O 2 and because of this process they are responsible for primary production in water (Matthews 2011, Duan et al. 2007). In addition, chl-a is the main indicator of phytoplankton biomass (Moses et a.l 2009, Zhang et al. 2015, Wozniak et al. 2014, Zhang et al. 2014) and could be used determine the water clarity (Salem et al. 2017b), because phytoplankton blooms cause important environmental problem, such as eutrophication and affect directly inland waters through the water quality and indirectly drinking water, fishing and swimming (Zhang et al. 2015). Whereas transparency of water indicates the light field, that penetrates the water and ensure sufficient amount of light for underwater ecosystems (Alikas 2016). Based on the optical properties, natural waters have been classified into two types in remote sensing applications: Case 1 and Case 2 waters. Case 1 waters are phytoplankton dominated waters and typically represent ’oceanic waters’ (Morel & Prieur 1977). Case 2 waters are optically complex waters with different concentrations of optically active substances (OAS) such as chl-a, colored dissolved organic matter (CDOM) and total suspended matter (TSM). Because of large and independent variations of OAS in Case 2 waters, the estimation of water quality parameters in these conditions are usually more complex than in Case 1 waters (Odermatt et al. 2012, Zhang et al. 2014). Because of high and independent chl-a, CDOM and TSM, lakes in Estonia represent Case 2 waters.
Show more

64 Read more

Band selection in Sentinel-2 satellite for agriculture applications

Band selection in Sentinel-2 satellite for agriculture applications

This paper develops a novel approach to analyze satellite remote sensing images, particularly Sentinel-2A satellite images using machine learning techniques. Three classification methods are studied and compared, namely index-based classification (NDVI, NDWI), specific relevant bands (RED, NIR, SWIR) based classification and all available bands based classification. By using a case study of land cover classification with four classes, it is shown that the method employing all available bands of Sentinel-2A satellite result in the best performance while the use of only three highly relevant bands also yields quite promising results. Overall the classification methods directly using specific relevant bands using supervised learning outperform the classic index based classification methods. Some limits of the index based classification could be removed by the direct use of spectral bands through the learned regression function between vegetation water content or soil moisture and certain bands of Sentinel-2A. The proposed algorithm can be applied to forest vegetation monitoring, vegetation physiological status detecting and irrigation decisions [20, 21].
Show more

6 Read more

Band selection in sentinel-2 satellite for agriculture applications

Band selection in sentinel-2 satellite for agriculture applications

For remote sensing applications, band information is of paramount importance in the phase of satellite data analysis and interpretation. The launch of Sentinel-2A is a key part of Global Monitoring for Environment and Security (GMES) program supported by the European Space Agency (ESA) and European Commission (EC) ensuring a better data continuity than other relevant satellites, such as SPOT and Landsat satellite series, due to its high spatial resolution and short revisit time. To obtain more retrieval information, its Multispectral Instrument (MSI) is an important component on this satellite as shown in Fig. 1. The MSI holds an anastigmatic telescope with three mirrors with a pupil diameter of about 150mm minimizing thermos-elastic distortions, and the optical design has been optimized to achieve state-of-the-art imaging quality across its 290km field of view [11-12]. MSI also features 13 spectral bands ranging from visible, NIR to SWIR at different resolutions. This configuration is selected as the best compromise between user requirements and mission performance. Four bands at 10m resolution meet the basic requirements for land classification; six bands at 20m resolution provide additional information on vegetation detecting. The remaining three bands at 60m contribute to atmospheric and geophysical parameters [12]. The launch of Sentinel-2B in March, 2017 shorten the revisit time into 5 days, which means Sentinel-2 series have the shortest revisit time among mainstream freely available satellites until now.
Show more

7 Read more

Enhanced Urban Sprawl Monitoring over the Entire District of Rome through Joint Analysis of ALOS AVNIR 2 and SENTINEL 2A Data

Enhanced Urban Sprawl Monitoring over the Entire District of Rome through Joint Analysis of ALOS AVNIR 2 and SENTINEL 2A Data

the ability to provide long term observations of the same area in the same way is one of the critical requirements for doing remote sensing for land use change. In this context the Sentinel program came to our aid. Within the frame of the Co- pernicus framework [13], ESA is in fact developing a series of missions that are complementary to one another, in order to provide continuity and long term, gap free data record [14]. The recent launch of the SENTINEL-2A satellite made available data with a minimum spatial resolution of 10 m, 13 spectral bands, wide acquisition coverage and short time revisits, which opened a large scale of new applications [15]. SENTINEL-2A data ensured basic observing continuity with ALOS mission over the area of study and improved the previous results on urban processes, by reducing the uncertainty of the discrimination of land cover classes and facilitating the photo-interpretation. The red, green, and blue spec- tral bands taken at 10 meters spatial resolution, allowed discrimination of indi- vidual features of large buildings, roads and small fields, namely to spot an awful lot of new details that are crucial in terms of land use change applications. The literature on urban sprawl is bigger than the scope of this paragraph but in depth state of the art was recently prepared by Prof. Reid H Ewing [16]. While several environmental indicators have been developed in the recent years, their deriva- tion from remote sensing and GIS data can be considered a relatively new field of research. To assess the impact of the urban sprawl over the entire district of Rome, the urban area profile indicator (UAP index) was adopted and, accor- dingly, a forecasting model was used to identify the areas with the higher risk of change within the reference period [6]. A number of different image processing, analysis and classification methods are available in order to generate land cover maps from high resolution optical remote sensing data; the choice of one me- thod rather than another depends on the physical characteristics of the study area, the temporal distribution of the available imagery and the nature of the classification problem itself [17]. For the present work we refer to our previous publications [6] and [7].
Show more

12 Read more

Detection of Temporary Flooded Vegetation Using Sentinel-1 Time Series Data

Detection of Temporary Flooded Vegetation Using Sentinel-1 Time Series Data

Abstract: The C-band Sentinel-1 satellite constellation enables the continuous monitoring of the Earth’s surface within short revisit times. Thus, it provides Synthetic Aperture Radar (SAR) time series data that can be used to detect changes over time regardless of daylight or weather conditions. Within this study, a time series classification approach is developed for the extraction of the flood extent with a focus on temporary flooded vegetation (TFV). This method is based on Sentinel-1 data, as well as auxiliary land cover information, and combines a pixel-based and an object-oriented approach. Multi-temporal characteristics and patterns are applied to generate novel times series features, which represent a basis for the developed approach. The method is tested on a study area in Namibia characterized by a large flood event in April 2017. Sentinel-1 times series were used for the period between September 2016 and July 2017. It is shown that the supplement of TFV areas to the temporary open water areas prevents the underestimation of the flood area, allowing the derivation of the entire flood extent. Furthermore, a quantitative evaluation of the generated flood mask was carried out using optical Sentinel-2 images, whereby it was shown that overall accuracy increased by 27% after the inclusion of the TFV.
Show more

23 Read more

A Global Analysis of Sentinel-2A, Sentinel-2B and Landsat-8 Data Revisit Intervals and Implications for Terrestrial Monitoring

A Global Analysis of Sentinel-2A, Sentinel-2B and Landsat-8 Data Revisit Intervals and Implications for Terrestrial Monitoring

The minimum and maximum revisit intervals illustrated in Figure 10, and in Sections 4.3 and 4.4, are of interest because they define the degree that sensor observations may be near-coincident and their greatest temporal separation, respectively. The minimum revisit interval in Figure 10c varies from 1418 min (23.63 h) to zero minutes, with a reporting precision of ±1 min, and along the transect the median value is 14 min (the same as the global median 14 min minimum revisit interval reported in Table 3). Evidently, these results and the one illustrated in Figure 7e, illustrate that, at most latitudes, near-coincident Landsat-8, Sentinel-2A and Sentinel-2B observations will occur at some point in the year. For such small revisit internals the surface land cover and condition can be expected to be the same, although the surface may not be observed under constant atmospheric conditions at windy locations where atmospheric contamination is spatially heterogeneous. If the data are reliably atmospherically corrected and cloud-masked then the near coincident sensor observations will be useful for a number of applications. These include inter-sensor calibration [32,33], statistical examination of among-sensor spectral band-pass differences [19,34,35] and characterization of surface reflectance anisotropy [36,37]. Near coincident sensor data can be obtained opportunistically by searching the Sentinel-2 and Landsat-8 data archives or can be
Show more

18 Read more

The Harmonized Landsat and Sentinel-2 Surface Reflectance Data Set

The Harmonized Landsat and Sentinel-2 Surface Reflectance Data Set

The HLS data set has the potential to support a wide variety of applications requiring high temporal and spatial resolutions optical data. Crop monitoring is maybe the most typical one. The 30-meter resolution is optimal for monitoring crop fields for most parts of the world (Lobell, 2013; Masek et al., 2006; Roy et al., 2014), but can be limiting for some small farming system (Bégué et al., 2018; White and Roy, 2015). The dense time series (80% of the global surface will have a potential revisit period of cloud-free observations of 8.4 days or fewer with the VC L8 + S-2A + S-2B) is adequate for detecting the sharp changes of surface, such as vegetation green-up or crop harvest. Figs. 14 to 16 show three time series examples of single pixels, highlighting the high potential of the HLS data set for crop monitoring at field scale. The dense TS (mostly for the first two examples) allows detection of rapid changes, such as a harvest event in early September 2016 in the New Mexico example (Fig. 15). The third case in France is typical of a cloudy area. While the TS is not as dense as the first two cases, most pheno- logical stages of this double cropping system are clearly identifiable.
Show more

17 Read more

Fusion of Landsat 8 OLI and Sentinel 2 MSI data

Fusion of Landsat 8 OLI and Sentinel 2 MSI data

Fusion of Landsat 8 and Sentinel-2 has great potential application value. First, for the Landsat 8 data acquired after the launch date of the Sentinel-2 satellite, they can be downscaled to 10 m and embedded to the available Sentinel-2 time-series data to produce finer temporal resolution data at 10 m spatial resolution, and more continuous global monitoring can be achieved to observe rapid changes on the Earth’s surface. The experimental results in Sections 3.1 and 3.3 where the Landsat 8 data were acquired temporally close to the Sentinel-2 data suggested that the proposed ATPRK-based fusion approach is suitable for coordinating the spatial resolutions of the two types of data for more continuous monitoring. Timely monitoring is critical in a wide range of applications, such as the urbanization process in highly developed cities [4], [5] and deforestation and forest degradation processes (for example in the Amazon rainforest where intervention is needed quickly following the detection of deforestation) [6]. Apart from LCLU changes, the finer temporal resolution Sentinel-2 data will also have great potential in monitoring rapid changes in vegetation phenology, especially in agricultural areas.
Show more

24 Read more

FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond

FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond

Abstract: Ever increasing data volumes of satellite constellations call for multi-sensor analysis ready data (ARD) that relieve users from the burden of all costly preprocessing steps. This paper describes the scientific software FORCE (Framework for Operational Radiometric Correction for Environmental monitoring), an ‘all-in-one’ solution for the mass-processing and analysis of Landsat and Sentinel-2 image archives. FORCE is increasingly used to support a wide range of scientific to operational applications that are in need of both large area, as well as deep and dense temporal information. FORCE is capable of generating Level 2 ARD, and higher-level products. Level 2 processing is comprised of state-of-the-art cloud masking and radiometric correction (including corrections that go beyond ARD specification, e.g., topographic or bidirectional reflectance distribution function correction). It further includes data cubing, i.e., spatial reorganization of the data into a non-overlapping grid system for enhanced efficiency and simplicity of ARD usage. However, the usage barrier of Level 2 ARD is still high due to the considerable data volume and spatial incompleteness of valid observations (e.g., clouds). Thus, the higher-level modules temporally condense multi-temporal ARD into manageable amounts of spatially seamless data. For data mining purposes, per-pixel statistics of clear sky data availability can be generated. FORCE provides functionality for compiling best-available-pixel composites and spectral temporal metrics, which both utilize all available observations within a defined temporal window using selection and statistical aggregation techniques, respectively. These products are immediately fit for common Earth observation analysis workflows, such as machine learning-based image classification, and are thus referred to as highly analysis ready data (hARD). FORCE provides data fusion functionality to improve the spatial resolution of (i) coarse continuous fields like land surface phenology and (ii) Landsat ARD using Sentinel-2 ARD as prediction targets. Quality controlled time series preparation and analysis functionality with a number of aggregation and interpolation techniques, land surface phenology retrieval, and change and trend analyses are provided. Outputs of this module can be directly ingested into a geographic information system (GIS) to fuel research questions without any further processing, i.e., hARD+. FORCE is open source software under the terms of the GNU General Public License v. >= 3, and can be downloaded from http://force.feut.de.
Show more

21 Read more

High-resolution urban aerosol monitoring using Sentinel -2 satellite images

High-resolution urban aerosol monitoring using Sentinel -2 satellite images

The study used different algorithms to retrieve AOD from Sentinel -2 images and evaluates their accuracies against AERONET measured AOD. While Sen2Cor and iCOR algorithms achieve better correlations with AERONET AOD for both stations, they fail to capture detailed spatial variations in AOD distribution. MAJA algorithm on the hand achieves lower correlations for both stations but shows detailed variations of AOD spatially. Though at a coarser spatial resolution of 300m and no data pixels due to masking out clouds, cloud shadows, snow and water bodies, Sentinel -3 SYN AOD product matches MAJA retrieved AOD and slight spatial variations in AOD values are visible. The algorithms also show the ability to identify temporal trends in AOD values similar to the ground measurements from AERONET stations. BOA reflectances are significant in AOD retrieval with stable surfaces such as in built-up surfaces in the urban region showing better agreements with AERONET AOD while in rural regions where the vegetation changes rapidly over time, the retrieved AOD shows lesser agreement for the three algorithms.
Show more

10 Read more

Deformation Monitoring Using Persistent Scatterer Interferometry and Sentinel-1 SAR Data

Deformation Monitoring Using Persistent Scatterer Interferometry and Sentinel-1 SAR Data

1. Introduction Persistent Scatterer Interferometry (PSI) is a group of Differential SAR Interferometry (DInSAR) techniques widely used to measure and monitor terrain deformations 1 . It uses large stacks of Synthetic Aperture Radar (SAR) images and suitable data modelling procedures that allow the estimation of different parameters. These parameters include the deformation time series, the average displacement rates and the so-called residual topographic error. It has been successfully used in a wide range of applications mainly related to the fields of urban, peri-urban and built 2,3,4,5 , subsidence and uplift 6,7,8,9 , landslides 10,11,12,13 , and geophysics 14,15,16,17 .
Show more

6 Read more

Deformation monitoring using Persistent Scatterer Interferometry and Sentinel-1 SAR data

Deformation monitoring using Persistent Scatterer Interferometry and Sentinel-1 SAR data

occur with the data acquired by the C-band sensor onboard the Sentinel-1 satellites. Sentinel-1A, launched on 3 April 2014, acquires interferometric C-band SAR data and offers an improved data acquisition capability with respect to previous C-band sensors (ERS-1/2, Envisat and Radarsat), increasing considerably the deformation monitoring potential. It acquires images covering 250 by 180 km with a revisiting cycle of 12 days in the Interferometric Wide Swath (IW) data acquisition mode, which will be reduced to 6 days when the images acquired by the Sentinel-1B satellite are available. The Sentinel-1 coverage might be essential for certain applications that might benefit from a wide-area PSI monitoring using C-band Sentinel-1 data. Moreover, this data allows an improvement of the coherence, mainly related to the high temporal sampling, which imply a reduced temporal decorrelation of the interferometric pair 26 . Finally, these data are free of charge, which represent an advantage with respect to X-band sensors such as
Show more

6 Read more

Detection of temporary flooded vegetation using Sentinel-1 time series data

Detection of temporary flooded vegetation using Sentinel-1 time series data

Abstract: The C-band Sentinel-1 satellite constellation enables the continuous monitoring of the Earth’s surface within short revisit times. Thus, it provides Synthetic Aperture Radar (SAR) time series data that can be used to detect changes over time regardless of daylight or weather conditions. Within this study, a time series classification approach is developed for the extraction of the flood extent with a focus on temporary flooded vegetation (TFV). This method is based on Sentinel-1 data, as well as auxiliary land cover information, and combines a pixel-based and an object-oriented approach. Multi-temporal characteristics and patterns are applied to generate novel times series features, which represent a basis for the developed approach. The method is tested on a study area in Namibia characterized by a large flood event in April 2017. Sentinel-1 times series were used for the period between September 2016 and July 2017. It is shown that the supplement of TFV areas to the temporary open water areas prevents the underestimation of the flood area, allowing the derivation of the entire flood extent. Furthermore, a quantitative evaluation of the generated flood mask was carried out using optical Sentinel-2 images, whereby it was shown that overall accuracy increased by 27% after the inclusion of the TFV.
Show more

23 Read more

Integrating openstreetmap data and sentinel-2 Imagery for classifying and monitoring informal settlements

Integrating openstreetmap data and sentinel-2 Imagery for classifying and monitoring informal settlements

We suggest that this research area could be explored more and future research could be done, building and improving on the results with emphasis on a better accurate representation of Informal settlements. Furthermore, other OSM features and parameters such as Roads and accessibility within the informal settlements or others could be used to better identify where the informality regions could be. This is because Informal settlements are usually characterized by narrow and short road segments this numerous dangles ant the end of the roads. Other parameters like proximity to hazardous areas like wetland and sewerage channels should be considered. This however, would however require much more detailed and complex land cover classification scheme as a source of information for this analysis, including identification of, vegetation types, difference between agricultural bare soil and bare soil areas in urban area or a construction sites and so on. If available, Digital Surface Model (DSM) could be used to more accurately identify and map informal settlements because inclusion of the third dimension of Elevation would help highlight more distinguishing characteristics of informal settlement buildings.
Show more

68 Read more

Building a data set over 12 globally distributed sites to support the development of agriculture monitoring applications with sentinel-2

Building a data set over 12 globally distributed sites to support the development of agriculture monitoring applications with sentinel-2

Aiming at developing an operational and globally relevant agriculture monitoring system is not straightforward. It raises several challenges. In spite of a relative standardization of the agriculture products and the large international market integration for the main commodities, the agricultural landscapes are highly diverse. In terms of landscape and agricultural practices, there is very little in common between the Red River Delta in Northern Vietnam with 1000 persons per square kilometer living from food production based on water management since centuries with three to four cropping cycles a year and the Southern part of Central African Republic where 8 persons per square kilometer live from shifting cultivation and forest harvesting. From the remote sensing point of view, being relevant over the whole range of agricultural systems will require dealing adequately with the global diversity of surface reflectance values to extract meaningful land cover information. Furthermore, the local heterogeneity of the agricultural practices (rotations, unusual crops, grassland edges, etc.) and the agro-meteorological variability will prevent the use of expected trajectories for crop discrimination and will require methodologies able to account for specific local conditions.
Show more

29 Read more

Sentinel-1 and Sentinel-2 Data Fusion for Mapping and Monitoring Wetlands

Sentinel-1 and Sentinel-2 Data Fusion for Mapping and Monitoring Wetlands

Abstract: Wetlands benefits can be summarized but are not limited to their ability to store floodwaters and improve water quality, providing habitats for wildlife and supporting biodiversity, as well as aesthetic values. Over the past few decades, remote sensing and geographical information technologies has proven to be a useful and frequent applications in monitoring and mapping wetlands. Combining both optical and microwave satellite data can give significant information about the biophysical characteristics of wetlands and wetlands` vegetation. Also, fusing data from different sensors, such as radar and optical remote sensing data, can increase the wetland classification accuracy. In this paper we investigate the ability of fusion two fine spatial resolution satellite data, Sentinel-2 and the Synthetic Aperture Radar Satellite, Sentinel-1, for mapping wetlands. As a study area in this paper, Balikdami wetland located in the Anatolian part of Turkey has been selected. Both Sentinel-1 and Sentinel-2 images require pre-processing before their use. After the pre-processing, several vegetation indices calculated from the Sentinel-2 bands were included in the data set. Furthermore, an object-based classification was performed. For the accuracy assessment of the obtained results, number of random points were added over the study area. In addition, the results were compared with data from Unmanned Aerial Vehicle collected on the same data of the overpass of the Sentinel-2, and three days before the overpass of Sentinel-1 satellite. The accuracy assessment showed that the results significant and satisfying in the wetland classification using both multispectral and microwave data. The statistical results of the fusion of the optical and radar data showed high wetland mapping accuracy, with an overall classification accuracy of approximately 90% in the object-based classification. Compared with the high resolution UAV data, the classification results give promising results for mapping and monitoring not just wetlands, but also the sub-classes of the study area. For future research, multi-temporal image use and terrain data collection are recommended.
Show more

12 Read more

Sentinel-2 Data Analysis and Comparison with UAV Multispectral Images for Precision Viticulture

Sentinel-2 Data Analysis and Comparison with UAV Multispectral Images for Precision Viticulture

Precision viticulture (PV) requires the use of technologies that can detect the spatial and temporal variability of vineyards and, at the same time, allow useful information to be obtained at sustainable costs. In order to develop a cheap and easy-to-handle operational monitoring scheme for PV, the aim of this work was to evaluate the possibility of using Sentinel-2 multispectral images for long-term vineyard monitoring through the Normalized Difference Vegetation Index (NDVI). Vigour maps of two vineyards located in northeastern Italy were computed from satellite imagery and compared with those derived from UAV multispectral images; their correspondence was evaluated from qualitative and statistical points of view. To achieve this, the UAV images were roughly resampled to 10 m pixel size in order to match the spatial resolution of the satellite imagery. Preliminary results show the potential use of open source Sentinel-2 platforms for monitoring vineyards, highlighting links with the information given in the agronomic bulletins and identifying critical areas for crop production. Despite the large differences in spatial resolution, the results of the comparison between the UAV and Sentinel-2 data were promising. However, for long-term vineyard monitoring at territory scale, further studies using multispectral sensor calibration and groundtruth data are required.
Show more

13 Read more

Sentinel-2 web platform for REDD+ monitoring.
Online web platform for browsing and processing Sentinel-2 data for forest cover monitoring over the Tropics

Sentinel-2 web platform for REDD+ monitoring. Online web platform for browsing and processing Sentinel-2 data for forest cover monitoring over the Tropics

2. the “Full, Free and Open” data policy /licensing scheme; 3. the global coverage of land with a guaranteed continuity (> 10 years) of observations. Tropical forest mapping and monitoring is a key application domain for EO due to the need for recurrent and frequent data to produce annual information on forest cover in humid and seasonal domains and regular information on forest disturbance processes. It benefits from long term consistent archives of Landsat imagery for forest area change, for instance in support to various mature and operational applications such as the Global Forest Watch (GFW) platform ( 1 ) of the World Resources Institute and the PRODES project ( 2 ) of the Brazilian National Space Agency. Previous attempts to integrate high resolution EO imagery into operational forest degradation mapping and monitoring have largely failed due to inadequate technical parameters, high costs and uncertain long term perspective. The EO use in this community is currently mostly limited to Landsat sensors (30 m), with global or
Show more

24 Read more

Review of Sentinel-2 applications

Review of Sentinel-2 applications

Eliakim Hamunyela in his doctoral thesis "Spatial monitoring of changes in tropical forests using observations from multiple satellites" talks about how to improve satellite monitoring of forest changes by addressing key challenges that hinder correct and timely spotting of disturbances in forests from satellite data. More specifically, the thesis assesses whether the problem is with the season, a small-scale omission and low-scale and low-noise forest disorders, the innate noise in the time series of satellite images and inter-sensory differences in the multi-sensory time series. Researches were accomplished in wet tropical forests in Brazil and dry tropical forests with a strong season in Bolivia. In addition, a distinction between the spatial context model and the seasonal model is done. [2]
Show more

17 Read more

Show all 10000 documents...