Paul Aplin

Sub-pixel land cover mapping for per-field classification

A method was developed to transform a soft land cover classification into hard land cover classes at the sub-pixel scale for subsequent per-field classification. First, image pixels were segmented using vector boundaries. Second, the pixel segments (ranked by area) were labelled with a land cover class (ranked by class typicality). Third, a hard per-field classification was generated by examining each polygon (representing a land cover parcel, or field) in its entirety (by grouping the fragments of the polygon contained within different image pixels) and assigning to it the modal land cover class. The accuracy of this technique was considerably higher than that of both a corresponding hard per-pixel classification and a perfield classification based on hard per-pixel classified imagery.

Fine spatial resolution satellite sensors for the next decade

Following the end of the Cold War governmental restrictions on the commercial availability of fine spatial resolution satellite sensor imagery have been relaxed world-wide. This, combined with marked reductions in the costs of developing, launching and operating satellites, has led to considerable research activity in this field by a number of private remote sensing organisations. Within the next few years, imagery with a spatial resolution as fine as 1 m in panchromatic mode and 4 m in multispectral mode will be available widely. This Letter presents a review of fine spatial resolution satellite sensors in operation or planned for operation within the next decade. Details of both commercial and governmental systems are provided. The emphasis is on commercially available data and so data collected for defence applications only are not included. A variety of both instrument and data specifications are highlighted, including spatial and spectral capabilities, and characteristics of viewing geometry, satellite orbit, data collection and supply. Typically, these systems are characterized not only by their fine spatial resolution, but also by high geometric precision, short revisit intervals and rapid data supply.

Remote sensing: ecology

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Spatial variation in land cover and choice of spatial resolution for remote sensing

Prior to acquiring remotely sensed imagery with which to map land cover investigators may wish to select an appropriate spatial resolution. Previously, statistics such as the local variance and scale variance have been used to facilitate this goal. However, where such statistics vary locally over the region of interest, their use in selecting a single spatial resolution may be undermined. The variogram and scale variance (plotted as a function of spatial resolution) were predicted for airborne multispectral imagery with a spatial resolution of 4 m of St Albans, Hertfordshire, UK and of Arundel, Sussex, UK. The remotely sensed response in the red and near-infrared wavelengths was found to vary appreciably both within and between broad land categories (such as urban, agricultural and semi-natural areas). These differences mean that where the subject of interest is a general region rather than a specific feature or object the mean local variance or scale variance over that region may be unhelpful in selecting a single spatial resolution. Further, differences observed between the red and near-infrared wavelengths have implications for users who wish to select a single spatial resolution for multispectral imagery.

On scales and dynamics in observing the environment

Natural and anthropogenic processes at the Earths surface operate at a range of spatial and temporal scales. Different scales of observation are required to match the spatial scales of the processes under observation. At the same time, the temporal sampling rate of the observing systems must be reconciled with the dynamics of the processes observed. Bringing together these issues requires insight, innovation and, inevitably, compromise. This paper reviews spatial and temporal considerations in remote sensing and introduces the papers in this Special Issue on ‘Scales and Dynamics in Observing the Environment’. The review comprises three main sections. The first section focuses on spatial variability in remote sensing, while the second section focuses on temporal variability in remote sensing. The third section links these two issues, focusing on the interplay of space and time in remote sensing. The review is primarily theoretical, explaining spatial and temporal properties of remote sensing and remotely sensed phenomena. Where appropriate, however, practical examples are included to demonstrate how remote sensing is used in environmental applications. Following the review, the papers included in the Special Issue are introduced, outlining their significance in the context of ‘Scales and Dynamics in Observing the Environment’.

Introduction to object-based landscape analysis

Current environmental challenges often require regular and wide-area monitoring, which in theory Earth observation (EO) can provide. Commonly, these challenges do not focus on individual point targets, as represented by image pixels, but require consideration of whole landscapes and assessment of features in broader spatial contexts. Object-based approaches, which operate at the scale of real-world objects rather than pixels, offer a means of analysing EO data in a realistic context and integrating associated ancillary information to support real-world applications. The development of object-based image analysis has accelerated over the past decade and can now be considered mainstream, with commercially available software and a wide user community. For full and rigorous consideration of the implementation of object-based analysis in environmental applications, we propose an extension of the discussion to object-based ‘landscape’ analysis. This article serves as an introduction to a Special Issue on this theme, drawing on a technical meeting held in 2009 at The University of Nottingham, UK. The meetings aim was to bring together practitioners in remote sensing, geographic information science (GIScience) and environmental science to identify best practice in the development and application of object-based landscape analysis techniques. The papers presented outline new opportunities for object-based landscape analysis, showing the expansion of object-centred classification studies beyond routine use of image data, engaging with fundamental GIScience concepts such as spatial accuracy and scale and demonstrating the wider and growing relevance for the EO, GIScience, landscape ecology and broader environmental science communities.

Mapping Complex Urban Land Cover from Spaceborne Imagery: The Influence of Spatial Resolution, Spectral Band Set and Classification Approach

Detailed land cover information is valuable for mapping complex urban environments. Recent enhancements to satellite sensor technology promise fit-for-purpose data, particularly when processed using contemporary classification approaches. We evaluate this promise by comparing the influence of spatial resolution, spectral band set and classification approach for mapping detailed urban land cover in Nottingham, UK. A WorldView-2 image provides the basis for a set of 12 images with varying spatial and spectral characteristics, and these are classified using three different approaches (maximum likelihood (ML), support vector machine (SVM) and object-based image analysis (OBIA)) to yield 36 output land cover maps. Classification accuracy is evaluated independently and McNemar tests are conducted between all paired outputs (630 pairs in total) to determine which classifications are significantly different. Overall accuracy varied between 35% for ML classification of 30 m spatial resolution, 4-band imagery and 91% for OBIA classification of 2 m spatial resolution, 8-band imagery. The results demonstrate that spatial resolution is clearly the most influential factor when mapping complex urban environments, and modern “very high resolution” or VHR sensors offer great advantage here. However, the advanced spectral capabilities provided by some recent sensors, coupled with contemporary classification approaches (especially SVMs and OBIA), can also lead to significant gains in mapping accuracy. Ongoing development in instrumentation and methodology offer huge potential here and imply that urban mapping opportunities will continue to grow.

STARS: A New Method for Multitemporal Remote Sensing

There is great potential for the development of remote sensing methods that integrate and exploit both multispectral and multitemporal information. This paper presents a new image processing method: Spectral-Temporal Analysis by Response Surface (STARS), which synthesizes the full information content of a multitemporal-multispectral remote sensing image data set to represent the spectral variation over time of features on the Earths surface. Depending on the application, STARS can be effectively implemented using a range of different models [e.g., polynomial trend surface (PTS) and collocation surface (CS)], exploiting data from different sensors, with varying spectral wavebands and acquiring data at irregular time intervals. A case study was used to test STARS, evaluating its potential to characterize sugarcane harvest practices in Brazil, specifically with and without preharvest straw burning. Although the CS model presented sharper and more defined spectral-temporal surfaces, abrupt changes related to the sugarcane harvest event were also well characterized with the PTS model when a suitable degree was set. Orthonormal coefficients were tested for both the PTS and CS models and performed more accurately than regular coefficients when used as input for three evaluated classifiers: instance based, decision tree, and neural network. Results show that STARS holds considerable potential for representing the spectral changes over time of features on the Earths surface, thus becoming an effective image processing method, which is useful not only for classification purposes but also for other applications such as understanding land-cover change. The STARS algorithm can be found at www.dsr.inpe.br/~mello.

Cover: Spatial and temporal remote sensing requirements for river monitoring

Rivers represent one of many dynamic environments that are routinely modelled and monitored using remote sensing. Given that rivers vary markedly in ‘size’, ‘shape’ and ‘rate of change’, as does th...

Improving estimates of tropical peatland area, carbon storage, and greenhouse gas fluxes

Our limited knowledge of the size of the carbon pool and exchange fluxes in forested lowland tropical peatlands represents a major gap in our understanding of the global carbon cycle. Peat deposits in several regions (e.g. the Congo Basin, much of Amazonia) are only just beginning to be mapped and characterised. Here we consider the extent to which methodological improvements and improved coordination between researchers could help to fill this gap. We review the literature on measurement of the key parameters required to calculate carbon pools and fluxes, including peatland area, peat bulk density, carbon concentration, above-ground carbon stocks, litter inputs to the peat, gaseous carbon exchange, and waterborne carbon fluxes. We identify areas where further research and better coordination are particularly needed in order to reduce the uncertainties in estimates of tropical peatland carbon pools and fluxes, thereby facilitating better-informed management of these exceptionally carbon-rich ecosystems.Our limited knowledge of the size of the carbon pool and exchange fluxes in forested lowland tropical peatlands represents a major gap in our understanding of the global carbon cycle. Peat deposits in several regions (e.g. the Congo Basin, much of Amazonia) are only just beginning to be mapped and characterised. Here we consider the extent to which methodological improvements and improved coordination between researchers could help to fill this gap. We review the literature on measurement of the key parameters required to calculate carbon pools and fluxes, including peatland area, peat bulk density, carbon concentration, above-ground carbon stocks, litter inputs to the peat, gaseous carbon exchange, and waterborne carbon fluxes. We identify areas where further research and better coordination are particularly needed in order to reduce the uncertainties in estimates of tropical peatland carbon pools and fluxes, thereby facilitating better-informed management of these exceptionally carbon-rich ecosystems.