Mapping Burned Areas in a Mediterranean Environment Using Soft Integration of Spectral Indices from High-Resolution Satellite Images

Abstract 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 method proposed here integrates several spectral indices into a fuzzy synthetic indicator of likelihood of burn. The indices are interpreted through fuzzy membership functions that have been derived with a partially data-driven approach exploiting training data and expert knowledge. The final map of fire-affected areas is produced by applying a region growing algorithm on the basis of seed pixels selected on a conservative threshold of the synthetic fuzzy score. The algorithm has been developed and tested on a set of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) scenes acquired over Southern Italy. Validation showed that the accuracy of the burned area maps is comparable or even better [overall accuracy (OA) > 90%, K > 0.76] than that obtained with approaches based on single index thresholds adapted to each image. The method described here provides an automatic approach for mapping fire-affected areas with very few false alarms (low commission error), whereas omission errors are mainly related to undetected small burned areas and are located in heterogeneous sparse vegetation cover

Seasonality of MODIS LST over Southern Italy and correlation with land cover, topography and solar radiation

AbstractLand 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 have been analyzed to quantify the influence of factors such as topography and the land cover on LST spatio-temporal variations. Results show that topography significantly influence LST variability as a function of the land cover and to a different extent for day-time and night-time data. Moreover, the relation between LST and the influential factors varies with the season during the year. This study contributes to a further understanding of the complex relationship between the spatio-temporal variability of the surface thermal conditions and its driving factors highlighting how these relationships might change within the year

Optical remote sensing of lakes: an overview on Lake Maggiore

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 techniques applied to lake water monitoring. Then, examples of applications focused on lake Maggiore, the second largest lake in Italy are discussed by presenting the temporal trend of chlorophyll-a (chl-a), suspended particulate matter (SPM), coloured dissolved organic matter (CDOM) and the z90 signal depth (the latter indicating the water depth from which 90% of the reflected light comes from) as estimated from the images acquired by the Medium Resolution Imaging Spectrometer (MERIS) in the pelagic area of the lake from 2003 to 2011. Concerning the chl-a trend, the results are in agreement with the concentration values measured during field surveys, confirming the good status of lake Maggiore, although occasional events of water deterioration were observed (e.g., an average increase of chl-a concentration, with a decrease of transparency, as a consequence of an anomalous phytoplankton occurred in summer 2011). A series of MERIS-derived maps (summer period 2011) of the z90 signal are also analysed in order to show the spatial variability of lake waters, which on average were clearer in the central pelagic zones. We expect that the recently launched (e.g., Landsat-8) and the future satellite missions (e.g., Sentinel-3) carrying sensors with improved spectral and spatial resolution are going to lead to a larger use of remote sensing for the assessment and monitoring of water quality parameters, by also allowing further applications (e.g., classification of phytoplankton functional types) to be developed

Towards an automated approach to map flooded areas from Sentinel-2 MSI data and soft integration of water spectral features

Abstract 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 Ordered Weighted Averaging (OWA) operators, which model user's decision attitude varying smoothly between optimistic and pessimistic approach. Output is a map of global evidence degree showing the plausibility of being flooded for each pixel of the input Sentinel-2 (S2) image. Algorithm set up and validation were carried out with data over three sites in Italy where water surfaces are extracted from stable water bodies (lakes and rivers), natural hazard flooding, and irrigated paddy rice fields. Validation showed more than satisfactory accuracy for the OR-like OWA operators (F-score > 0.90) with performance slightly decreased (F-scor

Assessing in-season crop classification performance using satellite data: a test case in Northern Italy

AbstractThis 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 a function of the crop map delivery time during the season. Overall accuracy (Kappa) exceeds 85% (0.83) starting from mid-July, five months before the end of the season, when maximum accuracy is reached (OA=92%, Kappa=0.91). Among crop types, rice is the most accurately classified, followed by forages, maize and arboriculture, while soybean or double crops can be confused with other classes

Exploitation of SAR and optical Sentinel data to detect rice crop and estimate seasonal dynamics of leaf area index

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 radiative transfer 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

Early season weed mapping in rice crops using multi-spectral UAV data

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 from images acquired by a Parrot Sequoia sensor mounted on a quadcopter, was classified through an unsupervised clustering algorithm; cluster labelling into â weed/no weed classes was achieved using geo-referenced observations. We tested the best set of input features among spectral bands, spectral indices and textural metrics. Weed mapping performance was assessed by calculating overall accuracy (OA) and, for the weed class, omission (OE) and commission errors (CE). Classification results were assessed under an alarmist approach in order to minimise the chance of overestimating weed coverage. Under this condition, we found that best results are provided by a set of spectral indices (OA= 96.5%, weed CE = 2.0%). The output weed map was aggregated to a grid layer of 5 x 5 m to simulate variable rate management units; a weed threshold was applied to identify the portion of the field to be subject to treatment with herbicides. Ancillary information on weed and crop conditions were derived over the grid cells to support precision agronomic management of rice crops at the early stage of growth

Implementaion of Regional Burnt Area Algorithms for the GBA2000 initiative

Technical Report - Global Burnt Area 200

A global inventory of burned areas at 1km resolution for he year 2000 derived from spot vegetation data

Biomass burning constitutes a major contribution to global emissions of carbon dioxide, carbon monoxide, methane, greenhouse gases and aerosols. Furthermore, biomass burning has an impact on health, transport, the environment and land use. Vegetation fires are certainly not recent phenomena and the impacts are not always negative. However, evidence suggests that fires are becoming more frequent and there is a large increase in the number of fires being set by humans for a variety of reasons. Knowledge of the interactions and feedbacks between biomass burning, climate and carbon cycling is needed to help the prediction of climate change scenarios. To obtain this knowledge, the scientific community requires, in the first instance, information on the spatial and temporal distribution of biomass burning at the global scale. This paper presents an inventory of burned areas at monthly time periods for the year 2000 at a resolution of 1 kilometer (km) and is available to the scientific community at no cost. The burned area products have been derived from a single source of satellite-derived images, the SPOT VEGETATION S1 1 km product, using algorithms developed and calibrated at regional scales by a network of partners. In this paper, estimates of burned area, number of burn scars and average size of the burn scar are described for each month of the year 2000. The information is reported at the country level. This paper makes a significant contribution to understanding the effect of biomass burning on atmospheric chemistry and the storage and cycling of carbon by constraining one of the main parameters used in the calculation of gas emissions

Downstream Services for Rice Crop Monitoring in Europe: From Regional to Local Scale

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 scale, while the second is dedicated to provide farmers with field-scale data useful to support more efficient and environmentally friendly crop practices. In this contribution, we describe the main characteristics of the system, in terms of overall architecture, technological solutions adopted, characteristics of the developed products, and functionalities provided to end users. Peculiarities of the system reside in its ability to cope with the needs of different stakeholders within a common platform, and in a tight integration between EO data processing and information retrieval, crop modeling, in situ data collection, and information dissemination. The ERMES system has been operationally tested in three European rice-producing countries (Italy, Spain, and Greece) during growing seasons 2015 and 2016, providing a great amount of near-real-time information concerning rice crops. Highlights of significant results are provided, with particular focus on real-world applications of ERMES products and services. Although developed with focus on European rice cultivations, solutions implemented in the ERMES system can be, and are already being, adapted to other crops and/or areas of the world, thus making it a valuable testing bed for the development of advanced, integrated agricultural monitoring systems