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

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

An Optimized System for the Classification of Meteorological Drought Intensity with Applications in Drought Frequency Analysis

The adequacy of reference drought intensity threshold levels based on deviations of monthly precipitation totals from normal climatological conditions is reconsidered. The motivation for this study is the observation that reference classification schemes are fixed for all climatological regions and threshold levels have been proposed without regard for the statistical distribution of accumulated precipitation in space and time. This misrepresentation of precipitation variability may lead to erroneous estimates of meteorological drought onset in specific areas where natural breaks in the cumulative distribution of monthly rainfall do not fit the generalized classification systems. Disproportionate estimates of drought onset may bias the frequency of events and confuse mitigation strategies. In this study, a new optimized classification system based on the non-parametric ``Fisher-Jenks'' algorithm is proposed for the estimation of drought intensity threshold levels from monthly rainfall totals. The optimized classification system is compared using the tabular accuracy index (TAI) to three fixed classification systems that are proposed in the literature and widely applied in the operational setting. Assessing the drought intensity classifications with optimized and fixed threshold levels, (i) six optimized categories most accurately divide precipitation observations into the most appropriate drought intensities, (ii) optimized thresholds always give considerably improved drought intensity category allocations over fixed thresholds with the same number of categories, and (iii) fixed thresholds underestimate the drought onset. A case study on the monthly and long-term drought frequency estimation for Latin America has been conducted for assessing the spatial link between drought intensity categories computed with the non-parametric ``Fisher-Jenks'' algorithm and different climate classifications. The results show a systematic match between climate variability in the region and spatial patterns of drought intensity.JRC.H.7-Climate Risk Managemen

A multitemporal and non-parametric approach for assessing the impacts of drought on vegetation greenness: A case study for Latin America

This study evaluates the relationship between the frequency and duration of meteorological droughts and the subsequent temporal changes on the quantity of actively photosynthesizing biomass (greenness) estimated from satellite imagery on rainfed croplands in Latin America. An innovative non-parametric and non-supervised approach, based on the Fisher-Jenks optimal classification algorithm, is used to identify multi-scale meteorological droughts on the basis of empirical cumulative distributions of 1, 3, 6, and 12-monthly precipitation totals. As input data for the classifier, we use the gridded GPCC Full Data Reanalysis precipitation time-series product, which ranges from January 1901 to December 2010 and is interpolated at the spatial resolution of 1° (decimal degree, DD). Vegetation greenness composites are derived from 10-daily SPOT-VEGETATION images at the spatial resolution of 1/112° DD for the period between 1998 and 2010. The time-series analysis of vegetation greenness is performed during the growing season with a non-parametric method, namely the seasonal Relative Greenness (RG) of spatially accumulated fAPAR. The Global Land Cover map of 2000 and the GlobCover maps of 2005/2006 and 2009 are used as reference data to select study cases only on geographic areas that did not undergo land cover changes during the analysis period. The multi-scale information is integrated at the lowest spatial resolution available, i.e. 1° DD, and the impacts of meteorological drought episodes on seasonal greenness of rainfed crops are assessed at the regional scale. Final results suggest that the agricultural cycle at the regional scale is more correlated with long-standing and uninterrupted small timescale drought conditions that occur prior to vegetation growing season than with isolated and short long-term timescale drought events.JRC.H.7-Climate Risk Managemen

Monitoring drought conditions and their uncertainties in Africa using TRMM data

The main objective of this study is to evaluate the uncertainties due to sample size associated with the estimation of the Standardized Precipitation Index (SPI) and their impact on the level of confidence in drought monitoring in Africa using high spatial resolution but short time series data. In order to do this, two different rainfall datasets, each available on a monthly basis, were analysed over four river basins in Africa (Oum er-Rbia, Limpopo, Niger, and Eastern Nile), as well as at continental level. The two precipitation datasets used were the Tropical Rainfall Measuring Mission (TRMM) satellite monthly rainfall product 3B43 and the Global Precipitation Climatology Centre (GPCC) full reanalysis gridded precipitation dataset. A non-parametric resampling bootstrap approach was used to compute the confidence bands associated with the SPI estimation, which are essential for making a qualified assessment of drought events. The comparative analysis of different datasets suggests that is feasible to use short time series of remote sensing precipitation data such as TRMM, that have a higher spatial resolution than other gridded precipitation data, for reliable drought monitoring over Africa. The proposed approach for drought monitoring has the potential to be used in support of decision making at both continental and sub-continental scales over Africa or over other regions that have a sparse distribution of rainfall measurement instruments.JRC.H.7-Climate Risk Managemen