Pedro J. Leitão

The EnMAP Spaceborne Imaging Spectroscopy Mission for Earth Observation

Imaging spectroscopy, also known as hyperspectral remote sensing, is based on the characterization of Earth surface materials and processes through spectrally-resolved measurements of the light interacting with matter. The potential of imaging spectroscopy for Earth remote sensing has been demonstrated since the 1980s. However, most of the developments and applications in imaging spectroscopy have largely relied on airborne spectrometers, as the amount and quality of space-based imaging spectroscopy data remain relatively low to date. The upcoming Environmental Mapping and Analysis Program (EnMAP) German imaging spectroscopy mission is intended to fill this gap. An overview of the main characteristics and current status of the mission is provided in this contribution. The core payload of EnMAP consists of a dual-spectrometer instrument measuring in the optical spectral range between 420 and 2450 nm with a spectral sampling distance varying between 5 and 12 nm and a reference signal-to-noise ratio of 400:1 in the visible and near-infrared and 180:1 in the shortwave-infrared parts of the spectrum. EnMAP images will cover a 30 km-wide area in the across-track direction with a ground sampling distance of 30 m. An across-track tilted observation capability will enable a target revisit time of up to four days at the Equator and better at high latitudes. EnMAP will contribute to the development and exploitation of spaceborne imaging spectroscopy applications by making high-quality data freely available to scientific users worldwide.

The EnMAP-Box—A Toolbox and Application Programming Interface for EnMAP Data Processing

The EnMAP-Box is a toolbox that is developed for the processing and analysis of data acquired by the German spaceborne imaging spectrometer EnMAP (Environmental Mapping and Analysis Program). It is developed with two aims in mind in order to guarantee full usage of future EnMAP data, i.e., (1) extending the EnMAP user community and (2) providing access to recent approaches for imaging spectroscopy data processing. The software is freely available and offers a range of tools and applications for the processing of spectral imagery, including classical processing tools for imaging spectroscopy data as well as powerful machine learning approaches or interfaces for the integration of methods available in scripting languages. A special developer version includes the full open source code, an application programming interface and an application wizard for easy integration and documentation of new developments. This paper gives an overview of the EnMAP-Box for users and developers, explains typical workflows along an application example and exemplifies the concept for making it a frequently used and constantly extended platform for imaging spectroscopy applications.

Assessment of land use factors associated with dengue cases in Malaysia using Boosted Regression Trees

The transmission of dengue disease is influenced by complex interactions among vector, host and virus. Land use such as water bodies or certain agricultural practices have been identified as likely risk factors for dengue because of the provision of suitable habitats for the vector. Many studies have focused on the land use factors of dengue vector abundance in small areas but have not yet studied the relationship between land use factors and dengue cases for large regions. This study aims to clarify if land use factors other than human settlements, e.g. different types of agricultural land use, water bodies and forest are associated with reported dengue cases from 2008 to 2010 in the state of Selangor, Malaysia. From the correlative relationship, we aim to generate a prediction risk map. We used Boosted Regression Trees (BRT) to account for nonlinearities and interactions between the factors with high predictive accuracies. Our model with a cross-validated performance score (Area Under the Receiver Operator Characteristic Curve, ROC AUC) of 0.81 showed that the most important land use factors are human settlements (model importance of 39.2%), followed by water bodies (16.1%), mixed horticulture (8.7%), open land (7.5%) and neglected grassland (6.7%). A risk map after 100 model runs with a cross-validated ROC AUC mean of 0.81 (±0.001 s.d.) is presented. Our findings may be an important asset for improving surveillance and control interventions for dengue.

Assessing Weather Effects on Dengue Disease in Malaysia

The number of dengue cases has been increasing on a global level in recent years, and particularly so in Malaysia, yet little is known about the effects of weather for identifying the short-term risk of dengue for the population. The aim of this paper is to estimate the weather effects on dengue disease accounting for non-linear temporal effects in Selangor, Kuala Lumpur and Putrajaya, Malaysia, from 2008 to 2010. We selected the weather parameters with a Poisson generalized additive model, and then assessed the effects of minimum temperature, bi-weekly accumulated rainfall and wind speed on dengue cases using a distributed non-linear lag model while adjusting for trend, day-of-week and week of the year. We found that the relative risk of dengue cases is positively associated with increased minimum temperature at a cumulative percentage change of 11.92% (95% CI: 4.41–32.19), from 25.4 °C to 26.5 °C, with the highest effect delayed by 51 days. Increasing bi-weekly accumulated rainfall had a positively strong effect on dengue cases at a cumulative percentage change of 21.45% (95% CI: 8.96, 51.37), from 215 mm to 302 mm, with the highest effect delayed by 26–28 days. The wind speed is negatively associated with dengue cases. The estimated lagged effects can be adapted in the dengue early warning system to assist in vector control and prevention plan.

Estimating Fractional Shrub Cover Using Simulated EnMAP Data: A Comparison of Three Machine Learning Regression Techniques

Anthropogenic interventions in natural and semi-natural ecosystems often lead to substantial changes in their functioning and may ultimately threaten ecosystem service provision. It is, therefore, necessary to monitor these changes in order to understand their impacts and to support management decisions that help ensuring sustainability. Remote sensing has proven to be a valuable tool for these purposes, and especially hyperspectral sensors are expected to provide valuable data for quantitative characterization of land change processes. In this study, simulated EnMAP data were used for mapping shrub cover fractions along a gradient of shrub encroachment, in a study region in southern Portugal. We compared three machine learning regression techniques: Support Vector Regression (SVR); Random Forest Regression (RF); and Partial Least Squares Regression (PLSR). Additionally, we compared the influence of training sample size on the prediction performance. All techniques showed reasonably good results when trained with large samples, while SVR always outperformed the other algorithms. The best model was applied to produce a fractional shrub cover map for the whole study area. The predicted patterns revealed a gradient of shrub cover between regions affected by special agricultural management schemes for nature protection and areas without land use incentives. Our results highlight the value of EnMAP data in combination with machine learning regression techniques for monitoring gradual land change processes.

Effects of geographical data sampling bias on habitat models of species distributions: a case study with steppe birds in southern Portugal

Habitat models of species distributions provide useful information about species and biodiversity spatial patterns, which form the basis of many ecological applications and management decisions such as the definition of conservation priorities and reserve selection. These models, however, are frequently based on existing datasets which have been collected in an unbalanced (biased) manner. In this study we investigated the effects of data sampling bias on model performance, interpretation and particularly spatial predictions. We collected a large steppe bird dataset in southern Portugal, following a carefully designed sampling scheme and then sub-sampled this dataset, roughly discarding between 80% and 90% of the observations, with varying degrees of geographical bias and random sampling. We characterised the data subsets in terms of data reduction and environmental bias. Multivariate adaptive regression splines (MARS) models were run on all datasets, and all the subset models compared with the baseline to assess the effect of the respective biases. We found that environmental bias in the datasets was very influential on the predicted spatial patterns of species occurrences. It is therefore important that special attention is paid to the quality of existing datasets used in habitat modelling, as well as the sampling design for collection of new data. Also, when modelling with biased datasets, the ecological interpretation of such models should be made with caution and explicit awareness of the existing bias.

Using Class Probabilities to Map Gradual Transitions in Shrub Vegetation from Simulated EnMAP Data

Monitoring natural ecosystems and ecosystem transitions is crucial for a better understanding of land change processes. By providing synoptic views in space and time, remote sensing data have proven to be valuable sources for such purposes. With the forthcoming Environmental Mapping and Analysis Program (EnMAP), frequent and area-wide mapping of natural environments by means of high quality hyperspectral data becomes possible. However, the amplified spectral mixing due to the sensor’s ground sampling distance of 30 m on the one hand and the patterns of natural landscapes in the form of gradual transitions between different land cover types on the other require special attention. Based on simulated EnMAP data, this study focuses on mapping shrub vegetation along a landscape gradient of shrub encroachment in a semi-arid, natural environment in Portugal. We demonstrate how probability outputs from a support vector classification (SVC) model can be used to extend a hard classification by information on shrub cover fractions. This results in a more realistic representation of gradual transitions in shrub vegetation maps. We suggest a new, adapted approach for SVC parameter selection: During the grid search, parameter pairs are evaluated with regard to the prediction of synthetically mixed test data, representing shrub to non-shrub transitions, instead of the hard classification of original, discrete test data. Validation with an unbiased, equalized random sampling shows that the resulting shrub-class probabilities from adapted SVC more accurately represent shrub cover fractions (mean absolute error/root mean squared error of 16.3%/23.2%) compared to standard SVC (17.1%/29.5%). Simultaneously, the discrete classification output was considerably improved by incorporating synthetic mixtures into parameter selection (averaged F1 accuracies increased from 72.4% to 81.3%). Based on our findings, the integration of synthetic mixtures into SVC parameterization allows the use of SVC for sub-pixel cover fraction estimation and, this way, can be recommended for deriving improved qualitative and quantitative descriptions of gradual transitions in shrub vegetation. The approach is therefore of high relevance for mapping natural ecosystems from future EnMAP data.

Monitoring Natural Ecosystem and Ecological Gradients: Perspectives with EnMAP

In times of global environmental change, the sustainability of human–environment systems is only possible through a better understanding of ecosystem processes. An assessment of anthropogenic environmental impacts depends upon monitoring natural ecosystems. These systems are intrinsically complex and dynamic, and are characterized by ecological gradients. Remote sensing data repeatedly collected in a systematic manner are suitable for describing such gradual changes over time and landscape gradients, e.g., through information on the vegetation’s phenology. Specifically, imaging spectroscopy is capable of describing ecosystem processes, such as primary productivity or leaf water content of vegetation. Future spaceborne imaging spectroscopy missions like the Environmental Mapping and Analysis Program (EnMAP) will repeatedly acquire high-quality data of the Earth’s surface, and will thus be extremely useful for describing natural ecosystems and the services they provide. In this conceptual paper, we present some of the preparatory research of the EnMAP Scientific Advisory Group (EnSAG) on natural ecosystems and ecosystem transitions. Through two case studies we illustrate the usage of spectral indices derived from multi-date imaging spectroscopy data at EnMAP scale, for mapping vegetation gradients. We thus demonstrate the benefit of future EnMAP data for monitoring ecological gradients and natural ecosystems.

Landscape makers and landscape takers: links between farming systems and landscape patterns along an intensification gradient

ContextAgricultural intensification is a leading cause of landscape homogenization, with negative consequences for biodiversity and ecosystem services. Conserving or promoting heterogeneity requires a detailed understanding of how farm management affects, and is affected by, landscape characteristics.ObjectivesWe assessed relationships between farming systems and landscape characteristics, hypothesising that less-intensive systems act as landscape takers, by adapting management to landscape constraints, whereas more intensive systems act as landscape makers, by changing the landscape to suit farming needs.MethodsWe mapped dominant farming systems in a region of southern Portugal: traditional cereal-grazed fallow rotations; specialization on annual crops; and specialization on either cattle or sheep. We estimated landscape metrics in 241 1-km2 buffers representing the farming systems, and analysed variation among and within systems using multivariate statistics and beta diversity metrics.ResultsLandscape composition varied among systems, with dominance by either annual crops (Crop system) or pastures (Sheep), or a mixture between the two (Traditional and Cattle). There was a marked regional gradient of local landscape heterogeneity, but this contributed little to variation among systems. Landscape beta diversity declined from the Sheep to the Crop system, and it was inversely related to agriculture intensity.ConclusionsLess intensive farming systems appeared compatible with a range of landscape characteristics (landscape takers), and may thus be particularly suited to agri-environmental management. More intensive systems appeared less flexible in terms of landscape characteristics (landscape makers), likely promoting regional homogenization. Farming systems may provide a useful standpoint to address the design of agri-environment schemes.

Mapping beta diversity from space: sparse Generalised Dissimilarity Modelling (SGDM) for analysing high-dimensional data

Summary 1. Spatial patterns of community composition turnover (beta diversity) may be mapped through generalised dissimilarity modelling (GDM). While remote sensing data are adequate to describe these patterns, the often high-dimensional nature of these data poses some analytical challenges, potentially resulting in loss of generality. This may hinder the use of such data for mapping and monitoring beta-diversity patterns. 2. This study presents Sparse Generalised Dissimilarity Modelling (SGDM), a methodological framework designed to improve the use of high-dimensional data to predict community turnover with GDM. SGDM consists of a two-stage approach, by first transforming the environmental data with a sparse canonical correlation analysis (SCCA), aimed at dealing with high-dimensional data sets, and secondly fitting the transformed data with GDM. The SCCA penalisation parameters are chosen according to a grid search procedure in order to optimise the predictive performance of a GDM fit on the resulting components. The proposed method was illustrated on a case study with a clear environmental gradient of shrub encroachment following cropland abandonment, and subsequent turnover in the bird communities. Bird community data, collected on 115 plots located along the described gradient, were used to fit composition dissimilarity as a function of several remote sensing data sets, including a time series of Landsat data as well as simulated EnMAP hyperspectral data. 3. The proposed approach always outperformed GDM models when fit on high-dimensional data sets. Its usage on low-dimensional data was not consistently advantageous. Models using high-dimensional data, on the other hand, always outperformed those using low-dimensional data, such as single-date multispectral imagery. 4. This approach improved the direct use of high-dimensional remote sensing data, such as time-series or hyperspectral imagery, for community dissimilarity modelling, resulting in better performing models. The good performance of models using high-dimensional data sets further highlights the relevance of dense time series and data coming from new and forthcoming satellite sensors for ecological applications such as mapping species beta diversity.