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The Sentinel-2 satellites are providing an unparalleled wealth of high-resolution remotely sensed information with a short revisit cycle, which is ideal for mapping burned areas both accurately and timely. However, the high detail and volume of information provided actually encumbers the automation of the mapping process, at least for the level of automation required to map systematically wildfires on a national level. This paper proposes a fully automated methodology for mapping burn scarsdoi:10.4236/jgis.2020.123014 fatcat:krw2dblgifgnrbhjawcdpnn3we
more »... g Sentinel-2 data. Information extracted from a pair of Sentinel-2 images, one pre-fire and one post-fire, is jointly used to automatically label a set of training patterns via two empirical rules. An initial pixel-based classification is derived using this training set by means of a Support Vector Machine (SVM) classifier. The latter is subsequently smoothed following a multiple spectral-spatial classification (MSSC) approach, which increases the mapping accuracy and thematic consistency of the final burned area delineation. The proposed methodology was tested on six recent wildfire events in Greece, selected to cover representative cases of the Greek ecosystems and to present challenges in burned area mapping. The lowest classification accuracy achieved was 92%, whereas Matthews correlation coefficient (MCC) was greater or equal to 0.85.
Chara Minakou was involved in the early steps of the research and conducted part of the reinterpretation. All the contributors were involved in the geoland2 project. ...doi:10.3390/rs70505012 fatcat:mbkhj5rgnfemtarhx3qclryvki
This paper presents and evaluates multitemporal LAI estimates derived from Sentinel-2A data on rice cultivated area identified using time series of Sentinel-1A images over the main European rice districts for the 2016 crop season. This study combines the information conveyed by Sentinel-1A and Sentinel-2A into a high-resolution LAI retrieval chain. Rice crop was detected using an operational multi-temporal rule-based algorithm, and LAI estimates were obtained by inverting the PROSAIL radiativedoi:10.3390/rs9030248 fatcat:2v6g3wfey5dhveeuuc7wv67dxy
more »... ransfer model with Gaussian process regression. Direct validation was performed with in situ LAI measurements acquired in coordinated field campaigns in three countries (Italy, Spain and Greece). Results showed high consistency between estimates and ground measurements, revealing high correlations (R 2 > 0.93) and good accuracies (RMSE < 0.83, rRMSE m < 23.6% and rRMSE r < 16.6%) in all cases. Sentinel-2A estimates were compared with Landsat-8 showing high spatial consistency between estimates over the three areas. The possibility to exploit seasonally-updated crop mask exploiting Sentinel-1A data and the temporal consistency between Sentinel-2A and Landsat-7/8 LAI time series demonstrates the feasibility of deriving operationally high spatial-temporal decametric multi-sensor LAI time series useful for crop monitoring. System (GTOS) and the Global Climate Observing System (GCOS)  . Defined as half of the total green leaf area per unit ground surface area , LAI plays a key role in land surface models  and can be used to address various agricultural issues, such as rice crop monitoring, yield forecasting and crop management  . Crop monitoring is necessary to identify the onset of stress conditions, which require actions for reducing their impact on crop yield. Anomaly drops in canopy LAI are indicators of plant stress conditions, which could lead to a decrease of plant production and to an increased senescence rate  . Rice yield forecasting is a crucial task for management and planning, and it can be addressed with both statistical and mathematical modeling approaches. Statistical approaches directly link crop biophysical variables, such as LAI, to crop yield. LAI is indeed recognized as the major morphological parameter for determining crop growth, and it is strongly correlated with crop productivity  . Crop models are able to simulate rice growing and are used to provide indications on crop status and to predict yields over large areas [7, 8] . However, crop models require information on soil, meteorological variable, crop parameters and management practices, which are not always available or practical to be obtained in a spatially distributed way and during the season  . Hence, the best way to simulate the real spatial-temporal differences in crop development of fields sowed the same day with the same variety is to assimilate exogenous observation, such as LAI maps, in the modeling solution . Accurate estimation of LAI has been shown to improve the accuracy of grain yield estimates  , and an operational application of this workflow for rice was successfully demonstrated in Asia in the framework of the RIICE (Remote sensing-based Information and Insurance for Crops in Emerging economies) project (http://www.riice.org/) where rice yield is estimated from the Oryza2000 model by assimilating LAI maps derived from synthetic aperture radar (SAR) images  . As far as precise crop management is concerned, LAI data have been found to be useful in new approaches for the determination of nitrogen concentration dilution curves, which are traditionally based on plant dry matter (PDM) estimation [13, 14] . Remotely-sensed data at decametric resolution (e.g., Sentinel-2A) are the sole source of information available to provide high-resolution (HR) LAI estimation on wide areas to exploit the nitrogen concentration dilution curve approach for optimal crop fertilization. In the last few years, the scientific community has made big efforts with the goal of providing reliable and accurate LAI estimates at local scales taking advantage of unmanned aerial vehicles and high-resolution sensors, such as Landsat  and SPOT5    . The recently launched Sentinel-2A satellite  provides well-suited spectral and temporal data for LAI retrieval at high-resolution in near real time, useful for assessing crop status and providing support in agro-practices at the parcel level. Many methods have been proposed and implemented in retrieval chains from Earth observation (EO) data to derive LAI estimates  . Empirical parametric algorithms have been developed based on calibrated relationships between vegetation indices and canopy biophysical variables  . On the other hand, non-parametric algorithms estimate biophysical variables using a training database containing pairs of the biophysical parameter and the associated spectral data  . In statistical approaches, concomitant in situ measurements of the biophysical parameter of interest and the associated spectral data from remote sensing platforms are used as a training database, whereas the hybrid approaches rely on a database generated by radiative transfer models (RTMs). RTMs describe the interactions between the incoming radiation, canopy elements and the background soil surface. The PROSAIL RTM has become one of the most popular and widely-used RTMs due to its consistency and robustness  . Hybrid approaches retrieve LAI by inverting RTM through machine learning techniques with a large number of methods [24, 25] . Among them, neural networks (NN)  and kernel-based methods [27, 28] are the most popular and used regression methods in remote sensing. State-of-the-art kernel-based methods, such as Gaussian process regression (GPR)  , provided encouraging results in the framework of biophysical parameter estimation outperforming other kernel methods and NN [18, 30] .
., 2011; Carre and Shiekhattar, 2011; Minaker et al., 2013; Staresincic et al., 2011; Zeng et al., 2018) . ... Within this region, the most conserved motif is an FPVARHRS sequence (aa 49-55) that is invariant in IYO homologues from all land plants and has a single aa substitution in the homologues from the algae Chara ...doi:10.3389/fpls.2019.01044 pmid:31552063 pmcid:PMC6748027 fatcat:lq64h72kprdjzligvzq2qfqyge
., An Empirical Approach to Estimate Soil Erosion Risk in Spain, Science of the Total Environment 409, (2011), 3114-3123 GİTAS, Ioannis Z, DOUROS Kostas, MİNAKOU Chara, Silleos, George N., KARYDAS, Christos ...doi:10.7827/turkishstudies.5358 fatcat:ut3vu6p5cfa7thuwkhailwl4jq
Singh, Raghuveer Šimek, Jiří Singh, Rahul Kumar Šimek, Milan Singh, Rajani Simeonov, Vasil Singh, Rajendra Simic, Marko Singh, Rajesh Simion, Georgiana Singh, Rajneesh Simitzi, Chara Singh, ... Miroslava Min, Changgi Milan, Steve Min, Cheonhong Milani, Alfredo Min, Kyungha Milani, Gabriele Min, Rui Milani, Paolo Minaee, Shervin Milanic, Matija Minafo, Giovanni Milano, Francesco Minak ...doi:10.3390/app11031108 fatcat:fcprlahz6jbvtnj3omkffng4uq
University Campus, 54124, Thessaloniki, Greece firstname.lastname@example.org 2 Aristotle University of Thessaloniki Department of Civil Engineering, Aristotle University Campus. 54124, Thessaloniki, Greece minak ... Structural Engineering, School of Civil Engineering, National Technical University of Athens, 15780, Athens, Greece email@example.com Structural Optimization Computing Platform (SOCP) and SCIA Software Chara ...doi:10.5281/zenodo.3561106 fatcat:ymvifna3kzg4vm66murscmxcj4