Harshness in image classification accuracy assessment.

Abstract Thematic mapping via a classification analysis is one of the most common applications of remote sensing. The accuracy of image classifications is, however, often viewed negatively. Here, it is suggested that the approach to the evaluation of image classification accuracy typically adopted in remote sensing may often be unfair, commonly being rather harsh and misleading. It is stressed that the widely used target accuracy of 85% can be inappropriate and that the approach to accuracy assessment adopted commonly in remote sensing is pessimistically biased. Moreover, the maps produced by other communities, which are often used unquestioningly, may have a low accuracy if evaluated from the standard perspective adopted in remote sensing. A greater awareness of the problems encountered in accuracy assessment may help ensure that perceptions of classification accuracy are realistic and reduce unfair criticism of thematic maps derived from remote sensing.

Semantic analysis of citizen sensing, crowdsourcing and VGI

This paper describes a semantic analysis of terms used to describe citizen sensing and crowdsourced data use in scientific analyses. It applies a latency analysis to journal abstracts downloaded from Scopus that matched one of number of terms related to crowd sourced data and citizen science. The latency analysis shows how the terms associated with crowdsourcing are related and how they have evolved over time.JRC.H.6-Digital Earth and Reference Dat

ERS-2 SAR soil moisture and herbaceous biomass monitoring across a semi-arid transect in Israel

Relationships between radar backscattering coefficient (σº) and environmental parameters of volumetric soil moisture and biomass were empirically investigated in two sites. These sites represent an annual rainfall change from 450 mm to 250 mm across a semiarid transition zone between Mediterranean and arid environments in Israel. A strong linear correlation was found between ground measurements of volumetric soil moisture taken place at the two sites in three different dates and corresponding radar backscatter intensities measured from ERS-2 SAR images. The slope and intercept values of the regression equation are similar to those reported in other regions of the world. Relationships between the radar backscatter and Herbaceous biomass were examined in areas of homogenous vegetation cover. Results show strong linear correlation between Herbaceous biomass measured in the field and the ERS-2 backscatter

A novel Border Identification algorithm based on an “Anti-Bayesian” paradigm

Border Identification (BI) algorithms, a subset of Prototype Reduction Schemes (PRS) aim to reduce the number of training vectors so that the reduced set (the border set) contains only those patterns which lie near the border of the classes, and have sufficient information to perform a meaningful classification. However, one can see that the true border patterns (“near” border) are not able to perform the task independently as they are not able to always distinguish the testing samples. Thus, researchers have worked on this issue so as to find a way to strengthen the “border” set. A recent development in this field tries to add more border patterns, i.e., the “far” borders, to the border set, and this process continues until it reaches a stage at which the classification accuracy no longer increases. In this case, the cardinality of the border set is relatively high. In this paper, we aim to design a novel BI algorithm based on a new definition for the term “border”. We opt to select the patterns which lie at the border of the alternate class as the border patterns. Thus, those patterns which are neither on the true discriminant nor too close to the central position of the distributions, are added to the “border” set. The border patterns, which are very small in number (for example, five from both classes), selected in this manner, have the potential to perform a classification which is comparable to that obtained by well-known traditional classifiers like the SVM, and very close to the optimal Bayes’ bound

Land cover classification using multi-temporal MERIS vegetation indices

The spectral, spatial, and temporal resolutions of Envisat's Medium Resolution Imaging Spectrometer (MERIS) data are attractive for regional- to global-scale land cover mapping. Moreover, two novel and operational vegetation indices derived from MERIS data have considerable potential as discriminating variables in land cover classification. Here, the potential of these two vegetation indices (the MERIS global vegetation index (MGVI), MERIS terrestrial chlorophyll index (MTCI)) was evaluated for mapping eleven broad land cover classes in Wisconsin. Data acquired in the high and low chlorophyll seasons were used to increase inter-class separability. The two vegetation indices provided a higher degree of inter-class separability than data acquired in many of the individual MERIS spectral wavebands. The most accurate landcover map (73.2%) was derived from a classification of vegetation index-derived data with a support vector machine (SVM), and was more accurate than the corresponding map derived from a classification using the data acquired in the original spectral wavebands

Mapping and the Citizen Sensor

Maps are a fundamental resource in a diverse array of applications ranging from everyday activities, such as route planning through the legal demarcation of space to scientific studies, such as those seeking to understand biodiversity and inform the design of nature reserves for species conservation. For a map to have value, it should provide an accurate and timely representation of the phenomenon depicted and this can be a challenge in a dynamic world. Fortunately, mapping activities have benefitted greatly from recent advances in geoinformation technologies. Satellite remote sensing, for example, now offers unparalleled data acquisition and authoritative mapping agencies have developed systems for the routine production of maps in accordance with strict standards. Until recently, much mapping activity was in the exclusive realm of authoritative agencies but technological development has also allowed the rise of the amateur mapping community. The proliferation of inexpensive and highly mobile and location aware devices together with Web 2.0 technology have fostered the emergence of the citizen as a source of data. Mapping presently benefits from vast amounts of spatial data as well as people able to provide observations of geographic phenomena, which can inform map production, revision and evaluation. The great potential of these developments is, however, often limited by concerns. The latter span issues from the nature of the citizens through the way data are collected and shared to the quality and trustworthiness of the data. This book reports on some of the key issues connected with the use of citizen sensors in mapping. It arises from a European Co-operation in Science and Technology (COST) Action, which explored issues linked to topics ranging from citizen motivation, data acquisition, data quality and the use of citizen derived data in the production of maps that rival, and sometimes surpass, maps arising from authoritative agencies

Double down on remote sensing for biodiversity estimation. A biological mindset

In the light of unprecedented planetary changes in biodiversity, real-time and accurate ecosystem and biodiversity assessments are becoming increasingly essential for informing policy and sustainable development. Biodiversity monitoring is a challenge, especially for large areas such as entire continents. Nowadays, spaceborne and airborne sensors provide information that incorporate wavelengths that cannot be seen nor imagined with the human eye. This is also now accomplished at unprecedented spatial resolutions, defined by the pixel size of images, achieving less than a meter for some satellite images and just millimeters for airborne imagery. Thanks to different modeling techniques, it is now possible to study functional diversity changes over different spatial and temporal scales. At the heart of this unifying framework are the “spectral species”—sets of pixels with a similar spectral signal—and their variability over space. The aim of this paper is to summarize the power of remote sensing for directly estimating plant species diversity, particularly focusing on the spectral species concept