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Spectral, temporal and spatial dimensions are difficult to model together when predicting in situ plant traits from remote sensing data. Therefore, machine learning algorithms solely based on spectral dimensions are often used as predictors, even when there is a strong effect of spatial or temporal autocorrelation in the data. A significant reduction in prediction accuracy is expected when algorithms are trained using a sequence in space or time that is unlikely to be observed again. The ensuing inability to generalise creates a necessity for ground-truth data for every new area or period, provoking the propagation of “single-use” models. This study assesses the impact of spatial autocorrelation on the generalisation of plant trait models predicted with hyperspectral data. Leaf Area Index (LAI) data generated at increasing levels of spatial dependency are used to simulate hyperspectral data using Radiative Transfer Models. Machine learning regressions to predict LAI at different levels of spatial dependency are then tuned (determining the optimum model complexity) using cross-validation as well as the NOIS method. The results show that cross-validated prediction accuracy tends to be overestimated when spatial structures present in the training data are fitted (or learned) by the model.

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O que é qualidade de vida e o quanto podemos medir dela? Pensa-se em qualidade de vida como resultado das políticas públicas e desenvolvimento de uma sociedade, onde os determinantes socioambientais se manifestam como atributo de seus atores. Ao mesmo tempo, pode-se entender esta idéia no outro extremo da análise, a partir da percepção de uma população protagonista de sua realidade, do que vem a ser qualidade de vida segundo ela mesma. Partindo-se dos aspectos conceituais de qualidade de vida, passou-se a adotar os conceitos de diferenciais intra-urbanos como a melhor maneira de caracterizar os desajustes e as desigualdades urbanas, para assim entender os componentes da iniqüidade desse meio. A primeira iniciativa marcou a utilização do método genebrino ou distancial. Hoje, já na segunda versão desse método, incorporou-se a esse contexto outras metodologias que possibilitam maior consistência de análise para ampliar a validade dessas medições. Soma-se a esse contexto, a análise de cluster e o Sistema de Informações Geográficas, tanto no cenário intra-urbano, quanto intermunicipal.

Remote sensing opens opportunities to assess spatial patterns on ecological data for a wide range of ecosystems. This information can be used to more effectively design sampling strategies for fieldwork, either to capture the maximum spatial dependence related to the ecological data or to completely avoid it. The sampling design and the autocorrelation observed in the field will determine whether there is a need to use a spatial model to predict ecological data accurately. In this article, we show the effects of different sampling designs on predictions of a plant trait, as an example of an ecological variable, using a set of simulated hyperspectral data with an increasing range of spatial autocorrelation. Our findings show that when the sample is designed to estimate population parameters such as mean and variance, a random design is appropriate even where there is strong spatial autocorrelation. However, in remote sensing applications, the aim is usually to predict characteristics of unsampled locations using spectral information. In this case, regular sampling is a more appropriated design. Sampling based on close pairs of points and clustered over a regular design may improve the accuracy of the training model, but this design generalizes poorly. The use of spatially explicit models improves the prediction accuracy significantly in landscapes with strong spatial dependence. However, such models have low generalization capacities to extrapolate to other landscapes with different spatial patterns. When the combination of design and size results in sample distances similar to the range of the spatial dependence in the field, it increases predictions uncertainty.