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A. G. Thomson

Bio: A. G. Thomson is an academic researcher. The author has contributed to research in topics: Land cover & Land information system. The author has an hindex of 8, co-authored 16 publications receiving 643 citations.

Papers
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Journal ArticleDOI
TL;DR: The Land Cover Map 2000 (LCM2000) as mentioned in this paper is a thematic classification of satellite image data covering the entire United Kingdom, which uses spectral segmentation of images to generate vector land parcels, which are then identified by the spectral classification of the image data in these parcels.
Abstract: Land Cover Map 2000 (LCM2000) is a thematic classification of satellite image data covering the entire United Kingdom. The map updates and substantially upgrades the Land Cover Map of Great Britain (LCMGB), made in 1990–92. This paper outlines the character of the map through a description of its specification, production and outputs. The paper is aimed at users of LCM2000 and derived data who need to understand more of the map and its characteristics. The paper also outlines plans for making data available to researchers and applied users.The most important development in LCM2000 was the spectral segmentation of images to generate vector land parcels. Land cover was then identified by the spectral classification of the image data in these parcels. Classification used specially developed procedures which exploited known spatial, spectral and contextual characteristics of land cover. The resultant GIS incorporates, within its vector structure, detailed attribute data which record parcel-based land ...

272 citations

Journal ArticleDOI
TL;DR: In this article, a parcel-based unsupervised classification approach was employed, using the first two Principal Components from 12 selected wavebands of HyMap data and a Digital Canopy Height Model extracted from LiDAR data.
Abstract: Tree and shrub species composition and vegetation structure are key components influencing the quality of woodland or forest habitat for a wide range of organisms. This paper investigates the unique thematic classes that can be derived using integrated airborne LiDAR and spectral data. The study area consists of a heterogeneous, semi‐natural broadleaf woodland on an ancient site and homogeneous broadleaf and conifer woodland on an adjoining plantation. A parcel‐based unsupervised classification approach was employed, using the first two Principal Components from 12 selected wavebands of HyMap data and a Digital Canopy Height Model extracted from LiDAR data. The resultant 52 data clusters were amalgamated into 10 distinct thematic classes that contain information on species composition and vegetation structure. The thematic classes are relevant to the National Vegetation Classification (NVC) scheme for woodlands and scrub of Great Britain. Furthermore, in distinguishing structural subdivisions within the s...

190 citations

Journal ArticleDOI
TL;DR: In this paper, a Land Cover Map 2007 (LCM2007) landscape features were derived from a generalised version of OS MasterMap to capture the required real-world objects.
Abstract: Earth Observation (EO) data is seen as a major source of information to characterise the Earth's surface, but is conventionally analysed using pixel-based approaches that do not incorporate the concept of landscape features or real-world objects. The UK land cover maps to date have been developed in an attempt to exploit landscape features to improve the quality and accuracy of their derived products. For Land Cover Map 2007 (LCM2007) landscape features will be derived from a generalised version of OS MasterMap to capture the required real-world objects. This paper describes the generalisation process that aligns the scale of the landscape features with the information content of high spatial resolution EO data as the first step in the production of LCM2007.

35 citations

Journal ArticleDOI
TL;DR: The Land Cover Map 2000 (LCM2000) as discussed by the authors is a comprehensive survey of UK broad habitats giving vector digital maps from segment-based classification of remotely sensed satellite data and the difficulties in applying a user-defined classification.

35 citations


Cited by
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Journal ArticleDOI
TL;DR: A tool, called estimation of scale parameter (ESP), that builds on the idea of local variance (LV) of object heterogeneity within a scene, enables fast and objective parametrization when performing image segmentation and holds great potential for OBIA applications.
Abstract: The spatial resolution of imaging sensors has increased dramatically in recent years, and so too have the challenges associated with extracting meaningful information from their data products. Object-based image analysis (OBIA) is gaining rapid popularity in remote sensing science as a means of bridging very high spatial resolution (VHSR) imagery and GIS. Multiscalar image segmentation is a fundamental step in OBIA, yet there is currently no tool available to objectively guide the selection of appropriate scales for segmentation. We present a technique for estimating the scale parameter in image segmentation of remotely sensed data with Definiens Developer®. The degree of heterogeneity within an image-object is controlled by a subjective measure called the 'scale parameter', as implemented in the mentioned software. We propose a tool, called estimation of scale parameter (ESP), that builds on the idea of local variance (LV) of object heterogeneity within a scene. The ESP tool iteratively generates image-objects at multiple scale levels in a bottom-up approach and calculates the LV for each scale. Variation in heterogeneity is explored by evaluating LV plotted against the corresponding scale. The thresholds in rates of change of LV (ROC-LV) indicate the scale levels at which the image can be segmented in the most appropriate manner, relative to the data properties at the scene level. Our tests on different types of imagery indicated fast processing times and accurate results. The simple yet robust ESP tool enables fast and objective parametrization when performing image segmentation and holds great potential for OBIA applications.

743 citations

Journal ArticleDOI
05 Jul 2013-Science
TL;DR: In this paper, the authors use spatially explicit models in conjunction with valuation methods to estimate comparable economic values for ecosystem services, taking account of climate change impacts, and show that highly significant value increases can be obtained from targeted planning by incorporating all potential ecosystem services and their values.
Abstract: Landscapes generate a wide range of valuable ecosystem services, yet land-use decisions often ignore the value of these services. Using the example of the United Kingdom, we show the significance of land-use change not only for agricultural production but also for emissions and sequestration of greenhouse gases, open-access recreational visits, urban green space, and wild-species diversity. We use spatially explicit models in conjunction with valuation methods to estimate comparable economic values for these services, taking account of climate change impacts. We show that, although decisions that focus solely on agriculture reduce overall ecosystem service values, highly significant value increases can be obtained from targeted planning by incorporating all potential services and their values and that this approach also conserves wild-species diversity.

735 citations

Journal ArticleDOI
TL;DR: In this paper, the relationship between momentary subjective wellbeing (SWB) and individuals' immediate environment within the UK was explored, finding that on average, participants are significantly happier outdoors in all green or natural habitat types than they are in urban environments.
Abstract: Links between wellbeing and environmental factors are of growing interest in psychology, health, conservation, economics, and more widely. There is limited evidence that green or natural environments are positive for physical and mental health and wellbeing. We present a new and unique primary research study exploring the relationship between momentary subjective wellbeing (SWB) and individuals’ immediate environment within the UK. We developed and applied an innovative data collection tool: a smartphone app that signals participants at random moments, presenting a brief questionnaire while using satellite positioning (GPS) to determine geographical coordinates. We used this to collect over one million responses from more than 20,000 participants. Associating GPS response locations with objective spatial data, we estimate a model relating land cover to SWB using only the within-individual variation, while controlling for weather, daylight, activity, companionship, location type, time, day, and any response trend. On average, study participants are significantly and substantially happier outdoors in all green or natural habitat types than they are in urban environments. These findings are robust to a number of alternative models and model specifications. This study provides a new line of evidence on links between nature and wellbeing, strengthening existing evidence of a positive relationship between SWB and exposure to green or natural environments in daily life. Our results have informed the UK National Ecosystem Assessment (NEA), and the novel geo-located experience sampling methodology we describe has great potential to provide new insights in a range of areas of interest to policymakers.

629 citations

Journal ArticleDOI
TL;DR: This communication identifies the key features of the Landsat program that have resulted in the extensive use of Landsat data for large area land cover mapping and monitoring, and uses this list as a basis for reviewing the current constellation of earth observation satellites to identify potential alternative data sources for large Area land cover applications.

522 citations

Journal ArticleDOI
TL;DR: A new concept of optically distinguishable functional types ('optical types') is proposed as a unique way to address the scale dependence of this problem and ensure more direct relationships between ecological information and remote sensing observations.
Abstract: Conceptually, plant functional types represent a classification scheme between species and broad vegetation types. Historically, these were based on physiological, structural and/or phenological properties, whereas recently, they have reflected plant responses to resources or environmental conditions. Often, an underlying assumption, based on an economic analogy, is that the functional role of vegetation can be identified by linked sets of morphological and physiological traits constrained by resources, based on the hypothesis of functional convergence. Using these concepts, ecologists have defined a variety of functional traits that are often context dependent, and the diversity of proposed traits demonstrates the lack of agreement on universal categories. Historically, remotely sensed data have been interpreted in ways that parallel these observations, often focused on the categorization of vegetation into discrete types, often dependent on the sampling scale. At the same time, current thinking in both ecology and remote sensing has moved towards viewing vegetation as a continuum rather than as discrete classes. The capabilities of new remote sensing instruments have led us to propose a new concept of optically distinguishable functional types ('optical types') as a unique way to address the scale dependence of this problem. This would ensure more direct relationships between ecological information and remote sensing observations.

488 citations