Daniela Stroppiana

Italian National Research Council | CNR · Institute for Electromagnetic Sensing of the Environment IREA

A study was carried out to investigate the effects of wildfires on lake water quality using a source dataset of 2024 lakes worldwide, covering different lake types and ecological settings. Satellite-derived datasets (Lakes_cci and Fire_cci) were used and a Source Pathway Receptor approach applied which was conceptually represented by fires (burned...

This report presents an overview of the 2021 wildfire season in the Mediterranean region on the basis of the data that are available in the European Forest Fire Information System (EFFIS ), which is complemented with a specific analysis of some of the most damaging fires during this campaign, performed by the FirEUrisk project. For the analysis of...

Lakes have been observed as sentinels of climate change. In the last decades, global warming and increasing aridity has led to an increase in both the number and severity of wildfires. This has a negative impact on lake catchments by reducing forest cover and triggering cascading effects in freshwater ecosystems. In this work we used satellite remo...

The availability of high-resolution reference datasets representing in space and time and with high accuracy areas affected by fires is strategic for the validation of remotely-sensed Burned Area (BA) products. This paper proposes a methodology designed to build a burned area reference dataset from Sentinel-2 (S2) images at continental scale by imp...

Coarse resolution sensors are not very sensitive at detecting small fire patches, making current estimations of global burned areas (BA) very conservative. Using medium or high-resolution sensors to generate BA products becomes then a priority, particularly in areas where fires tend to be small and frequent. Building on previous work that developed...

The paper proposes a fully automatic algorithm approach to map burned areas from remote sensing characterized by human interpretable mapping criteria and explainable results. This approach is partially knowledge-driven and partially data-driven. It exploits active fire points to train the fusion function of factors deemed influential in determining...

Sentinel-2 (S2) multi-spectral instrument (MSI) images are used in an automated approach built on fuzzy set theory and a region growing (RG) algorithm to identify areas affected by fires in Mediterranean regions. S2 spectral bands and their post- and pre-fire date (Dpost-pre) difference are interpreted as evidence of burn through soft constraints o...

The paper proposes a transparent approach for mapping the status of environmental phenomena from multisource information based on both soft computing and machine learning. It is transparent, intended as human understandable as far as the employed criteria, and both knowledge and data-driven. It exploits remote sensing experts’ interpretations to de...

The paper proposes a scalable fuzzy approach for mapping the status of the environment integrating several distinct models exploiting geo big data. The process is structured into two phases: the first one can exploit products yielded by distinct models of remote sensing image interpretation defined in the scientific literature, and knowledge of dom...

Providing relatively fine spatial resolution multispectral data, Landsat-8, Landsat-7 (L8 and L7, respectively) and Sentinel-2 (S2) from 2013 to 2018 have been used in this study for enabling high-frequency monitoring of water quality of two small (the smaller with an area of 1.6 km²) freshwater dammed reservoirs. Located in Sardinia (Italy) and Cr...

In this work we propose an approach for mapping flooded areas from Sentinel-2 MSI (Multispectral Instrument) data based on soft fuzzy integration of evidence scores derived from both band combinations (i.e. Spectral Indices - SIs) and components of the Hue, Saturation and Value (HSV) colour transformation. Evidence scores are integrated with Ordere...

Lodging is a major yield-reducing factors in wheat, causing reductions up to 80%. Timely detection of lodging can reduce its impacts and support proper decisions regarding expected yield, crop price or its insurance. Since the incidence of lodging is heterogeneous within a field, very high-resolution remote sensing data can be viable for accurate a...

In this study we exploit UAV data for estimating Fractional Vegetation Cover (FVC) of maize crop at the early stages of the growing season. UAV survey with a MicaSense RedEdge multispectral sensor was carried out on July 13th, 2017 over a maize field in Italy; simultaneous RGB in situ pictures were collected to build a reference dataset of FVC over...

Rice mapping products were derived from Sentinel-1A and Landsat-8 OLI multi-temporal imagery over Northern Italy at the early stages of the 2015 growing season. A rule-based algorithm was applied to synthetic statistical metrics (TSDs-Temporal Spectra Descriptors) computed from temporal datasets of optical spectral indices and SAR backscattering co...

The aim of this work is to assess the utility of combining Sentinel-2A and Landsat (Landsat-7 ETM+ and Landsat-8 OLI) LAI time series for rice crop monitoring. LAI maps were produced in three countries (Italy, Spain and Greece) using state-of-the-art machine learning algorithms trained on simulated radiative transfer modelling data specifically gen...

In this work a dataset of maps of Chl-a derived from different sensors (MERIS-OLI-MSI-OLCI) satellite images is used to evaluate water quality of the four most important Italian subalpine lakes (Garda, Maggiore, Iseo, Como) in the period 2003-2018. In order to produce Chl-a concentration maps, imagery needs to be processed along a processing chain,...

This paper aims at investigating the use of microwave and optical images for the detection and characterization of fire scars in vegetated areas. To cope with this issue, Sentinel-1 (S-1) C-band synthetic aperture radar (SAR), and multispectral Sentinel-2 (S-2) multispectral data are used. First, a consolidated fuzzy-based methodology, specifically...

The present short paper describes the functionalities of a desktop tool enabling non-expert users to develop downstream services for the periodic download and processing of data from Sentinel sources. Index Terms: downstream service, flooded area mapping, Sentinel data, spectral indicators.

In this article, we propose an automatic procedure for classification of UAV imagery to map weed presence in rice paddies at early stages of the growing cycle. The objective was to produce a weed map (common weeds and cover crop remnants) to support variable rate technologies for site-specific weed management. A multi-spectral ortho-mosaic, derived...

This paper aims at investigating the potential of Sentinel-1 C-band synthetic aperture radar (SAR) observations for detecting fire scars in vegetated areas at regional scale. A comprehensive analysis of the backscattering coefficients is carried out. The experimental analysis is conducted by analyzing the scenario of the Sardinia Island, which is o...

The triangle method has been applied to derive a weekly indicator of evaporative fraction on vegetated areas in a temperate region in Northern Italy. Daily MODIS Aqua Land Surface Temperature (MYD11A1) data has been combined with air temperature maps and 8-day composite MODIS NDVI (MOD13Q1/MYD13Q1) data to estimate the Evaporative Fraction (EF) at...

The ERMES agromonitoring system for rice cultivations integrates EO data at different resolutions, crop models, and user-provided in situ data in a unified system, which drives two operational downstream services for rice monitoring. The first is aimed at providing information concerning the behavior of the current season at regional/rice district...

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....

This study investigated the feasibility of delivering a crop type map early during the growing season. Landsat 8 OLI multi-temporal data acquired in 2013 season were used to classify seven crop types in Northern Italy. The accuracy achieved with four supervised algorithms, fed with multi-temporal spectral indices (EVI, NDFI, RGRI), was assessed as...

This contribution proposes a customizable SDI architecture to create, map, manage and share VGI in a full interoperable way by exploiting external knowledge on the context for which VGI is created, consisting of both domain ontologies and a representation of the entities of interest. The whole process of VGI creation by a smart app, VGI deploy on t...

Currently, the best practice to support land planning calls for the development of Spatial Data Infrastructures (SDI) capable of integrating both geospatial datasets and time series information from multiple sources, e.g., multitemporal satellite data and Volunteered Geographic Information (VGI). This paper describes an original OGC standard intero...

The application of an integrated monitoring tool to assess and understand the effects of annually occurring forest fires is presented, with special emphasis to Mediterranean and Temperate Continental zones of Europe. The distinctive features of the information conveyed by optical and microwave remote sensing data have been firstly investigated, and...

The aim of this paper is to investigate how optical and Synthetic Aperture Radar (SAR) data can be combined in an integrated multi-source framework to identify burned areas at the regional scale. The proposed approach is based on the use of fuzzy sets theory and a region-growing algorithm. Landsat TM and (C-band) ENVISAT Advanced Synthetic Aperture...

Agriculture is a global issue nowadays. At the European level, it is a sector, in which we are investing many resources. In particular, the Agri-Food sector plays a central role in the policies of the European Commission and the Horizon 2020 research and innovation program, as well as being the main theme of Expo 2015 that will be held in Milan, Lo...

Plant diseases are responsible for major economic losses in the agricultural industry worldwide. Monitoring plant health and detecting pathogen early are essential to reduce disease spread and facilitate effective management practices. DNA-based and serological methods now provide essential tools for accurate plant disease diagnosis, in addition to...

This paper describes a mapping project carried out using both optical and SAR data on an agricultural area in northern Italy where the main crops are corn, rice and wheat. Temporal trends of backscatter and reflectance, given by the variations in vegetation growth, soil conditions and agricultural practices were analyzed and interpreted thanks to t...

Land Surface Temperature (LST) is a key variable in the interactions and energy fluxes between the Earth surface and the atmosphere. Satellite data provide consistent, continuous and spatially distributed information on the Earth's surface conditions among which LST. Ten years of NASA-MODIS day-time and night-time 1 km LST data over Southern Italy...

Optical satellite remote sensing represents an opportunity to integrate traditional methods for assessing water quality of lakes: strengths of remote sensing methods are the good spatial and temporal coverage, the possibility to monitor many lakes simultaneously and the reduced costs. In this work we present an overview of optical remote sensing te...

In Southern Europe wildfires can be a key factor for the ecosystem dynamics and they can seriously threaten human lives and infrastructures (1). Monitoring can support post-fire management (e.g. to identify the location and assess the extent of the burned surfaces, to evaluate the damage to the forest stands, to follow vegetation recovery andto...

Mediterranean forests are every year affected by wildfires which have a significant effect on the ecosystem. Mapping burned areas is an important field of application for optical remote sensing techniques and several methodologies have been developed in order to improve mapping accuracy. We developed an automated procedure based on spectral indices...

Studies of the impact of human activity on vegetation dynamics of the Sahelian belt of Africa have been recently re-invigorated by new scientific findings that highlighted the primary role of climate in the drought crises of the 1970s–1980s. Time series of satellite observations revealed a re-greening of the Sahelian belt that indicates no notewort...

The Curonian Lagoon, the largest in Europe (total surface area 1584 km2), is located in the southern part of the Baltic Sea, from which it is separated by a narrow sand bar and to which it is connected by the Klaipeda Straits. It is characterised by shallow eutrophic waters (mean depth 3.8 m) with average low salinity (<5‰) due to the nutrient-rich...

Spatial assessment of environmental phenomena at regional/global scale involves the analysis and fusion of multiple, complex, multidisciplinary, and large-scale information. Since very often reliable models of such phenomena are lacking, the “syndrome approach” has been adapted to this purpose. In this context, there is a strong need for frameworks...

Large wildfires in forests of southern European countries such as Portugal, Spain, Greece, France and Italy are one key ecological disturbance of the Mediterranean environment. Optical data have been largely used for burned area mapping and literature provides an extensive reference for the typical spectral signal of burns and the methodologies app...

The paper proposes to model environmental syndromes based on a soft revision of bipolar information, namely consisting of a set of contextual conditions constraining the flourishing of the syndrome (negative information), and a typical pattern of notable symptoms (positive information) that are indeed proxies of observations of the syndrome occurre...

A straightforward way to map burned areas from remotely sensed imagery is to integrate partial evidence of burn provided by multiple spectral indices (SIs). Our approach relies on fuzzy set theory to generate integrated layers of overall positive evidence (PE) and negative evidence (NE) scores. In order to reduce commission errors, we propose the u...

Since fire is a major threat to forests and wooded areas in the Mediterranean environment of Southern Europe, systematic regional fire monitoring is a necessity. Satellite data constitute a unique cost-effective source of information on the occurrence of fire events and on the extent of the area burned. Our objective is to develop a (semi-)automate...

CO emissions due to biomass burning can be represented as raster maps computed from satellite observations obtained from sensors of different types, on board of different platforms, exploiting algorithms which combine input data in different ways. Hence, different CO emission “products” are available and it is important to define comparison tools a...

This study presents the application of a Light Use Efficiency model that exploits MODIS satellite data to assess rice yield in Italy. Field experimental data acquired in 2003 and 2004 were used to define model’s parameters (Radiation Use Efficiency and Harvest Index) and to calibrate the relation between MODIS-EVI and the fraction of Absorbed Photo...

survival of local populations especially in rangeland, as happened during the dramatic food crisis in the 70-80s caused by severe drought. This work has been carried out in the framework of the EU FP7 Geoland2 project as a contribution to the ECOWAS component (Economic Community Of West African States) of the AMESD (African Monitoring of the Enviro...

The lakes of the European perialpine region constitute a large water reservoir, which is threatened by the anthropogenic pressure altering water quality. The Water Framework Directive of the European Commission aims to protect water resources and monitoring is seen as an essential step for achieving this goal. Remote sensing can provide frequent da...

The hydrology of tropical forests play a key role in watershed processes such as soil erosion, streamflow and ground water recharge. However, tropical forests of Africa are least investigated due to the poor network for data acquisition. Earth Observations can fill this gap by providing consistent time series of data. We analyzed trends of rainfall...

Ill-known environmental phenomena are often modeled by means of multisource spatial data fusion. Generally, these fusion strategies have to cope with distinct kinds of uncertainty, related to the ill-defined knowledge of the phenomenon, the lack of classified data, the distinct trust of the information sources, the imprecision of the observed varia...

Ill-known environmental phenomena are often modeled by means of multi-source spatial data fusion. Generally, these fusion strategies have to cope with distinct kinds of uncertainty, related to the ill-defined knowledge of the phenomenon, the lack of classified data, the distinct trust of the information sources, the imprecision of the observed vari...

We compare five global inventories of monthly CO emissions named VGT, ATSR, MODIS, GFED3 and MOPITT based on remotely sensed active fires and/or burned area products for the year 2003. The objective is to highlight similarities and differences by focusing on the geographical and temporal distribution and on the emissions for three broad land cover...

This article presents a new method for burned area mapping using high-resolution satellite images in the Mediterranean ecosystem. In such a complex environment, high-resolution satellite images represent an appropriate data source for identifying fire-affected areas, and single postfire data are often the only available source of information. The m...

Studies of impact of human activity on the vegetation dynamics in the Sahel belt of Africa are recently re-invigorated due to a new scientific findings that highlighted the primary role of climate in the drought crises of the 70s-80s. Time series of satellite observations allowed identifying re-greening of the Sahel belt that indicates no sensible...

Natural Resource Monitoring in Africa (NARMA) is one of the Core Information Services of EU-FP7 project Geoland2 addressing important sectoral policies that concern with the development of an environmental monitoring capacity over African countries for the needs of the European Commission (EC) services and for regional and continental EC partners i...