Collinearity refers to the non independence of predictor variables, usually in a regression-type analysis. It is a common feature of any descriptive ecological data set and can be a problem for parameter estimation because it inflates the variance of regression parameters and hence potentially leads to the wrong identification of relevant predictors in a statistical model. Collinearity is a severe problem when a model is trained on data from one region or time, and predicted to another with a different or unknown structure of collinearity. To demonstrate the reach of the problem of collinearity in ecology, we show how relationships among predictors differ between biomes, change over spatial scales and through time. Across disciplines, different approaches to addressing collinearity problems have been developed, ranging from clustering of predictors, threshold-based pre-selection, through latent variable methods, to shrinkage and regularisation. Using simulated data with five predictor-response relationships of increasing complexity and eight levels of collinearity we compared ways to address collinearity with standard multiple regression and machine-learning approaches. We assessed the performance of each approach by testing its impact on prediction to new data. In the extreme, we tested whether the methods were able to identify the true underlying relationship in a training dataset with strong collinearity by evaluating its performance on a test dataset without any collinearity. We found that methods specifically designed for collinearity, such as latent variable methods and tree based models, did not outperform the traditional GLM and threshold-based pre-selection. Our results highlight the value of GLM in combination with penalised methods (particularly ridge) and threshold-based pre-selection when omitted variables are considered in the final interpretation. However, all approaches tested yielded degraded predictions under change in collinearity structure and the ‘folk lore’-thresholds of correlation coefficients between predictor variables of |r| >0.7 was an appropriate indicator for when collinearity begins to severely distort model estimation and subsequent prediction. The use of ecological understanding of the system in pre-analysis variable selection and the choice of the least sensitive statistical approaches reduce the problems of collinearity, but cannot ultimately solve them.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1600-0587.2012.07348.x

Collinearity: a review of methods to deal with it and a simulation study evaluating their performance

Image classification is a complex process that may be affected by many factors. This paper examines current practices, problems, and prospects of image classification. The emphasis is placed on the summarization of major advanced classification approaches and the techniques used for improving classification accuracy. In addition, some important issues affecting classification performance are discussed. This literature review suggests that designing a suitable image-processing procedure is a prerequisite for a successful classification of remotely sensed data into a thematic map. Effective use of multiple features of remotely sensed data and the selection of a suitable classification method are especially significant for improving classification accuracy. Non-parametric classifiers such as neural network, decision tree classifier, and knowledge-based classification have increasingly become important approaches for multisource data classification. Integration of remote sensing, geographical information systems (GIS), and expert system emerges as a new research frontier. More research, however, is needed to identify and reduce uncertainties in the image-processing chain to improve classification accuracy.

/pdf/a-survey-of-image-classification-methods-and-techniques-for-5bc5ltr9dq.pdf

A survey of image classification methods and techniques for improving classification performance

Automated Water Extraction Index: a new technique for surface water mapping using Landsat imagery.

Remote sensing of impervious surfaces in the urban areas: Requirements, methods, and trends

Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley

Spectral discrimination of vegetation types in a coastal wetland

Imaging Spectrometry and Vegetation Science

The spectral reflectance from eight species of rangeland grasses in the Masai Mara Nature Reserve, Kenya, was measured using a laboratory-based spectrometer. There were statistically significant differences in the spectral reflectance between species-a result which is encouraging for future work on identifying, classifying, mapping and monitoring rangeland ecosystems from hyperspectral imagery. To date, hyperspectral imagery has been available only through airborne scanners, but the European Space Agency and the United States' National Aeronautics and Space Administration (NASA) both plan satellite missions. The second part of this paper describes the acquisition and analysis of hyperspectral data (CASI) coincident with ground plots. In these plots, the mix of grass species varied from pure (monospecific) patches through to mixes of four to five different species. Evidence is presented indicating that some species may be identified on the image, based on the laboratory-obtained spectra.

Exploring spectral discrimination of grass species in African rangelands.

Remote sensing can be defined as the acquisition of physical data of an object with a sensor that has no direct contact with the object itself. Photography of the Earth's surface dates back to the early 1800s, when in 1839 Louis Daguerre publicly reported results of images from photographic experiments. In 1858 the first aerial view from a balloon was produced and in 1910 Wilber Smith piloted the plane that acquired motion pictures of Centocelli in Italy. Image photography was collected on a routine basis during both world wars; during World War I1 non-visible parts of the electromagnetic (EM) spectrum were used for the first time and radar technology was introduced. In 1960s, the first meteorological satellite was launched, but actual image acquisition from space dates back to earlier times with various spy satellites. In 1972, with the launch of the earth observation land satellite Landsat 1 (renamed from ERTS-l), repetitive and systematic observations were acquired. Many dedicated earth observation missions followed Landsat 1 and in 1980 NASA started the development of high spectral resolution instruments (hyperspectral remote sensing) covering the visible and shortwave infrared portions of the EM spectrum, with narrow bands allowing spectra of pixels to be imaged (Goetz et ul. 1985). Simultaneously in the field of active microwave remote sensing, research led to the development of multi-polarization radar systems and interferometric systems (Massonnet et ul. 1994). The turn of the millennium marks the onset of a new era in remote sensing when many experimental sensors and system approaches will be mounted on satellites, thereby providing ready access to data on a global scale. Interferometric systems will provide global digital elevation models, while spaceborne hyperspectral systems will allow detailed spectrophysical measurements at almost any part of the earth's surface. This chapter provides an overview of existing and planned satellite-based systems subdivided into the categories of high spatial resolution systems, high spectral resolution systems, high temporal resolution systems and radar systems (Figure 3.1). More technical details of some of these systems can be found in Kramer (1996). For readers requiring details of existing remote sensing systems as well as historical image archives, please refer to the references and internet links provided at the end of the chapter. The different sensor systems are catalogued within the internet links provided according to the order in which they are treated in the text. A brief discussion on the various application fields for the sensor types will follow the technical description of the instruments. The chapter provides a few classical references that serve as a starting point for further studies without

New environmental remote sensing systems

We report on studies that successfully map the distribution of plant species as well as parameters indicative of the quality of forage for herbivores. We show that rangeland and wetland species and types may be discriminated and mapped using GIS and hyperspectral remote sensing. Using artificial medium, as well as field experiments, insect herbivore growth is positively related to nitrogen content, while significantly higher abundance of large herbivores occurs on nutrient enriched sites in southern Africa. Plant nitrogen concentration is shown to be significantly related to a shift in the red edge as well as key wavelength absorption points. Finally the reflectance of other leaf biochemicals associated with forage quality (P, K, Mg, Ca) are also discriminated and mapped..

Modelling herbivore grazing resources using hyperspectral remote sensing and GIS

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