The contribution of multitemporal information from multispectral satellite images for automatic land cover classification at the national scale

Thesis submitted to the Instituto Superior de Estatística e Gestão de Informação da Universidade Nova de Lisboa in partial fulfillment of the requirements for the Degree of Doctor of Philosophy in Information Management – Geographic Information SystemsImaging and sensing technologies are constantly evolving so that, now, the latest generations of satellites commonly provide with Earth’s surface snapshots at very short sampling periods (i.e. daily images). It is unquestionable that this tendency towards continuous time observation will broaden up the scope of remotely sensed activities. Inevitable also, such increasing amount of information will prompt methodological approaches that combine digital image processing techniques with time series analysis for the characterization of land cover distribution and monitoring of its dynamics on a frequent basis. Nonetheless, quantitative analyses that convey the proficiency of three-dimensional satellite images data sets (i.e. spatial, spectral and temporal) for the automatic mapping of land cover and land cover time evolution have not been thoroughly explored. In this dissertation, we investigate the usefulness of multispectral time series sets of medium spatial resolution satellite images for the regular land cover characterization at the national scale. This study is carried out on the territory of Continental Portugal and exploits satellite images acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) and MEdium Resolution Imaging Spectrometer (MERIS). In detail, we first focus on the analysis of the contribution of multitemporal information from multispectral satellite images for the automatic land cover classes’ discrimination. The outcomes show that multispectral information contributes more significantly than multitemporal information for the automatic classification of land cover types. In the sequence, we review some of the most important steps that constitute a standard protocol for the automatic land cover mapping from satellite images. Moreover, we delineate a methodological approach for the production and assessment of land cover maps from multitemporal satellite images that guides us in the production of a land cover map with high thematic accuracy for the study area. Finally, we develop a nonlinear harmonic model for fitting multispectral reflectances and vegetation indices time series from satellite images for numerous land cover classes. The simplified multitemporal information retrieved with the model proves adequate to describe the main land cover classes’ characteristics and to predict the time evolution of land cover classes’individuals

An empirical standardized soil moisture index for agricultural drought assessment from remotely sensed data

AbstractWe propose a simple, spatially invariant and probabilistic year-round Empirical Standardized Soil Moisture Index (ESSMI) that is designed to classify soil moisture anomalies from harmonized multi-satellite surface data into categories of agricultural drought intensity. The ESSMI is computed by fitting a nonparametric empirical probability density function (ePDF) to historical time-series of soil moisture observations and then transforming it into a normal distribution with a mean of zero and standard deviation of one. Negative standard normal values indicate dry soil conditions, whereas positive values indicate wet soil conditions. Drought intensity is defined as the number of negative standard deviations between the observed soil moisture value and the respective normal climatological conditions. To evaluate the performance of the ESSMI, we fitted the ePDF to the Essential Climate Variable Soil Moisture (ECV SM) v02.0 data values collected in the period between January 1981 and December 2010 at South–Central America, and compared the root-mean-square-errors (RMSE) of residuals with those of beta and normal probability density functions (bPDF and nPDF, respectively). Goodness-of-fit results attained with time-series of ECV SM values averaged at monthly, seasonal, half-yearly and yearly timescales suggest that the ePDF provides triggers of agricultural drought onset and intensity that are more accurate and precise than the bPDF and nPDF. Furthermore, by accurately mapping the occurrence of major drought events over the last three decades, the ESSMI proved to be spatio-temporal consistent and the ECV SM data to provide a well calibrated and homogenized soil moisture climatology for the region. Maize, soybean and wheat crop yields in the region are highly correlated (r>0.82) with cumulative ESSMI values computed during the months of critical crop growing, indicating that the nonparametric index of soil moisture anomalies can be used for agricultural drought assessment

Multi-Disciplinary Forest Fire Danger Assessment in Europe: The Potential to Integrate Long-Term Drought Information

A key motivation for multi-disciplinary collaborations is the inclusion of data and knowledge from contributing disciplines for the further development of existing models. The objective of this research is to evaluate the potential of using drought information from the European Drought Observatory (EDO) to complement the forest fire danger assessment of the European Forest Fire Information System (EFFIS). Drought conditions are provided through the Standardized Precipitation Index (SPI), which is a spatially invariant and probabilistic year-round index based on precipitation alone. For verifying the hypothesis that drought information can improve the danger assessment of forest fires, we statistically analyse the correspondence between multi-timescale drought condition information with the incidence of forest fires. Within this paper, we perform a detailed comparative analysis of the SPI frequencies for burnt areas with the respective SPI frequencies for the total study area during the same period. The research is carried out in the Iberian Peninsula for the reference year 2009, using the burnt areas mapped by the EFFIS Rapid Damage Assessment. The results clearly show that the frequencies of burnt areas in Iberian Peninsula relate to the regions with abnormal 24-month accumulated precipitation totals, as mapped by the SPI. This suggests that the long-term lack of water contributes to vegetation dryness in the region and thereby increases its risk of fire danger. The added value of including drought information in the fire danger assessment lies in particular outside the forest fire season, when it provides complementary information on areas under risk that are not necessarily marked with a high fire risk following the risk assessment of EFFIS. Based on the results of the study, we suggest an operational integration of drought information coming from EDO into EFFIS using the existing web service infrastructure

JRC Experience on the Development of Drought Information Systems

From the definition of drought to its monitoring and assessment, this report summarizes the main steps towards an integrated drought information system. Europe, Africa and Latin America are examples, based on the experience of the JRC, that illustrate the challenges for establishing continental drought observatory initiatives. The document is structured in the following way: first an introduction explains what drought is and gives some examples of its impact in society; secondly the framework for establishing a drought monitoring system is described giving examples on the European Drought Observatory and on on-going activities in Africa and Latin America; thirdly the fundamental data and information for measuring drought is described; finally the setting up of an Integrated Drought Information System is discussed and two recent case studies, on Europe and on the Horn of Africa, are presented to illustrate the concept.JRC.H.7-Climate Risk Managemen

Using mixed objects in the training of object-based image classifications

Image classification for thematic mapping is a very common application in remote sensing, which is sometimes realized through object-based image analysis. In these analyses, it is common for some of the objects to be mixed in their class composition and thus violate the commonly made assumption of object purity that is implicit in a conventional object-based image analysis. Mixed objects can be a problem throughout a classification analysis, but are particularly challenging in the training stage as they can result in degraded training statistics and act to reduce mapping accuracy. In this paper the potential of using mixed objects in training object-based image classifications is evaluated. Remotely sensed data were submitted to a series of segmentation analyses from which a range of under- to over-segmented outputs were intentionally produced. Training objects were then selected from the segmentation outputs, resulting in training data sets that varied in terms of size (i.e. number of objects) and proportion of mixed objects. These training data sets were then used with an artificial neural network and a generalized linear model, which can accommodate objects of mixed composition, to produce a series of land cover maps. The use of training statistics estimated based on both pure and mixed objects often increased classification accuracy by around 25% when compared with accuracies obtained from the use of only pure objects in training. So rather than the mixed objects being a problem, they can be an asset in classification and facilitate land cover mapping from remote sensing. It is, therefore, desirable to recognize the nature of the objects and possibly accommodate mixed objects directly in training. The results obtained here may also have implications for the common practice of seeking an optimal segmentation output, and also act to challenge the widespread view that object-based classification is superior to pixel-based classification

Use of intra-annual satellite imagery time-series for land cover characterization purpose

International audienc

Global projections of drought hazard in a warming climate: a prime for disaster risk management

Projections of drought hazard (dH) changes have been mapped from five bias-corrected climate models and analyzed at the global level under three representative concentration pathways (RCPs). The motivation for this study is the observation that drought risk is increasing globally and the effective regulation of prevention and adaptation measures depends on dH magnitude and its distribution for the future. Based on the Weighted Anomaly of Standardized Precipitation index, dH changes have been assessed for mid-(2021–2050) and late-century (2071–2099). With a few exceptions, results show a likely increase in global dH between the historical years (1971–2000) and both future time periods under all RCPs. Notwithstanding this worsening trend, it was found that projections of dH changes for most regions are neither robust nor significant in the near-future. By the end of the century, greater increases are projected for RCPs describing stronger radiative forcing. Under RCP8.5, statistically significant dH changes emerge for global Mediterranean ecosystems and the Amazon region, which are identified as possible hotspots for future water security issues. Taken together, projections of dH changes point towards two dilemmas: (1) in the near-term, stake-holders are left worrying about projected increasing dH over large regions, but lack of actionable model agreement to take effective decisions related to local prevention and adaptation initiatives; (2) in the long-term, models demonstrate remarkable agreement, but stake-holders lack actionable knowledge to manage potential impacts far distant from actual human-dominated environments. We conclude that the major challenge for risk management is not to adapt human populations or their activities to dH changes, but to progress on global initiatives that mitigate their impacts in the whole carbon cycle by late-century.Fil: Carrão, Hugo. European Commission, Joint Research Centre; ItaliaFil: Naumann, Gustavo. European Commission, Joint Research Centre; Italia. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Barbosa, Paulo. European Commission, Joint Research Centre; Itali

Mapping global patterns of drought risk: An empirical framework based on sub-national estimates of hazard, exposure and vulnerability

A global map of drought risk has been elaborated at the sub-national administrative level. The motivation for this study is the observation that little research and no concerted efforts have been made at the global level to provide a consistent and equitable drought risk management framework for multiple regions, population groups and economic sectors. Drought risk is assessed for the period 2000-2014 and is based on the product of three independent determinants: hazard, exposure and vulnerability. Drought hazard is derived from a non-parametric analysis of historical precipitation deficits at the 0.5°; drought exposure is based on a non-parametric aggregation of gridded indicators of population and livestock densities, crop cover and water stress; and drought vulnerability is computed as the arithmetic composite of high level factors of social, economic and infrastructural indicators, collected at both the national and sub-national levels. The performance evaluation of the proposed models underlines their statistical robustness and emphasizes an empirical resemblance between the geographic patterns of potential drought impacts and previous results presented in the literature. Our findings support the idea that drought risk is driven by an exponential growth of regional exposure, while hazard and vulnerability exhibit a weaker relationship with the geographic distribution of risk values. Drought risk is lower for remote regions, such as tundras and tropical forests, and higher for populated areas and regions extensively exploited for crop production and livestock farming, such as South-Central Asia, Southeast of South America, Central Europe and Southeast of the United States. As climate change projections foresee an increase of drought frequency and intensity for these regions, then there is an aggravated risk for global food security and potential for civil conflict in the medium- to long-term. Since most agricultural regions show high infrastructural vulnerability to drought, then regional adaptation to climate change may begin through implementing and fostering the widespread use of irrigation and rainwater harvesting systems. In this context, reduction in drought risk may also benefit from diversifying regional economies on different sectors of activity and reducing the dependence of their GDP on agriculture.Fil: Carrão, Hugo. European Commission. Joint Research Centre; ItaliaFil: Naumann, Gustavo. European Commission. Joint Research Centre; Italia. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Barbosa, Paulo. European Commission. Joint Research Centre; Itali

Parametric model for intra-annual reflectance time series

International audienc

Land cover characterization through parametric modeling of intra-annual reflectance time series: a comparative study with MERIS data

International audienc