Ross A. Hill

The UK Land Cover Map 2000: Construction of a parcel-based vector Map from satellite images

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 cover, plus information on class probabilities, data on within-parcel heterogeneity, information on landscape structure and context, cover information from LCMGB, together with a record of each parcels processing history.

Quantifying canopy height underestimation by laser pulse penetration in small-footprint airborne laser scanning data

There is a well-reported tendency for canopy height to be underestimated in small-footprint airborne laser scanning (ALS) data of coniferous woodland. This is commonly explained by a failure to record treetops because of insufficient ALS sampling density. This study examines the accuracy of canopy height estimates retrieved from small-footprint ALS data of broadleaf woodland. A novel field sampling method was adopted to collect reference canopy upper surface measurements of known horizontal (x, y) and vertical (z) position that had sub-metre accuracy. By investigating the z differences between ALS and reference canopy measurements with matching x and y locations, the effects of ALS sampling density were removed from the analysis. For raw point-sample ALS data, a negative bias of 0.91 m for sample shrub canopies and 1.27 m for sample tree canopies was observed. These results suggest that for broadleaf woodland, a small-footprint laser pulse hitting the upper surface of a canopy often advances into the canopy before reflecting a signal strong enough to be detected by the scanner as a first return. The depth of laser pulse penetration will vary with canopy structural characteristics and ALS device configuration. Interpolation of the point-sample ALS canopy measurements into a grid-based digital canopy height model (DCHM) propagated the observed errors, resulting in a negative bias of 1.02 m for shrub canopies and 2.12 m for tree canopies. Here the sampling density in relation to canopy surface roughness was important.

Mapping woodland species composition and structure using airborne spectral and LiDAR data

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 species‐based NVC classes, the thematic classification provides greater information for quantifying woodland habitat. The classes show degeneration from and regeneration to mature woodland communities and thus reflect the underlying processes of vegetation succession and woodland management. This thematic classification is ecologically relevant and is a forward development in woodland maps created from remote sensing data.

Remote sensing and the future of landscape ecology

Landscape ecology focuses on the analysis of spatial pattern and its relationship to ecological processes. As a scientific discipline, landscape ecology has grown rapidly in recent years, supported by developments in GIS and spatial analysis techniques. Although remote sensing data are widely employed in landscape ecology research, their current and potential roles have not been evaluated critically. To provide an overview of current practice, 438 research papers published in the journal Landscape Ecology for the years 2004—2008 were examined for information about use of remote sensing. Results indicated that only 36% of studies explicitly mentioned remote sensing. Of those that did so, aerial photographs and Landsat satellite sensor images were most commonly used, accounting for 46% and 42% of studies, respectively. The predominant application of remote sensing data across these studies was for thematic mapping purposes. This suggests that landscape ecologists have been relatively slow to recognize the potential value of recent developments in remote sensing technologies and methods. The review also provided evidence of a frequent lack of key detail in studies recently published in Landscape Ecology , with 75% failing to provide any assessment of uncertainty or error relating to image classification and mapping. It is suggested that the role of remote sensing in landscape ecology might be strengthened by closer collaboration between researchers in the two disciplines, by greater integration of diverse remote sensing data with ecological data, and by increased recognition of the value of remote sensing beyond land-cover mapping and pattern description. This is illustrated by case studies drawn from Latin America (focusing on forest loss and fragmentation) and the UK (focusing on habitat quality for woodland birds). Such approaches might improve the analytical and theoretical rigour of landscape ecology, and be applied usefully to issues of outstanding societal interest, such as the impacts of environmental change on biodiversity and ecosystem services.

Classification of tropical forest classes from Landsat TM data.

Abstract The spectral separability of thirteen topical vegetation classes, including twelve forest types, was assessed. Although the thirteen classes could not be classified to a high accuracy the results of a set of supervised and unsupervised classifications revealed that three groups of classes were highly separable; a classification of the three groups by a discriminant analysis had an accuracy of 92·20 per cent. These three spectrally separable groups also corresponded closely to ecological groups identified from an ordination of data on tree species contained within a detailed ground data set. On the basis of the class separability analyses the three spectrally separable groups were mapped, with an accuracy of 94·84 per cent, from Landsat TM data by a maximum likelihood classification. It was apparent that some of the errors in this classification could be resolved through the use of contextual information and ancillary information, particularly on topography.

Image segmentation for humid tropical forest classification in Landsat TM data

Humid tropical forest types have low spectral separability in Landsat TM data due to highly textured reflectance patterns at the 30m spatial resolution. Two methods of reducing local spectral variation, low-pass spatial filtering and image segmentation, were examined for supervised classification of 10 forest types in TM data of Peruvian Amazonia. The number of forest classes identified at over 90% accuracy increased from one in raw imagery to three in filtered imagery, and six in segmented imagery. The ability to derive less generalised tropical forest classes may allow greater use of classified imagery in ecology and conservation planning.

Cover: Predicting habitat quality for Great Tits (Parus major) with airborne laser scanning data

1. Overview Habitat quality is a fundamental concept in ecology that is difficult to quantify objectively. In avian ecology, habitat quality is often inferred from demographic rates (Riddington and Gosler 1995), patterns of territory occupancy and stability (Matthysen 1990), or measurements of resource availability (Seki and Takano 1998). Vegetation structure is an important component of bird habitat quality (Fuller and Henderson 1992, Beier and Drennan 1997). For woodland birds, mapping the three-dimensional complexity of their habitat by field survey can be a timeconsuming and difficult task. Airborne laser scanning (ALS) is a remote sensing technique, operating on a principle of light detection and ranging (LiDAR) that supplies fine-grained information on vegetation structure at a woodland scale (Næsset 2002). The cover image (and figure 1) shows a predictive map of reproductive performance in Great Tits (Parus major) based on a woodland canopy height model derived from ALS data. Since Great Tits feed their young on treedwelling lepidopteran larvae, canopy structure influences habitat quality via effects on both food abundance and its availability to the birds. Such remote quantification of habitat quality could greatly enhance our ability to predict impacts of changing environmental pressures on biodiversity.

The Application of Lidar in Woodland Bird Ecology

Habitat quality is fundamental in ecology, but is difficult to quantify. Vegetation structure is a key characteristic of avian habitat, and can play a significant role in influencing habitat quality. Airborne lidar provides a means of measuring vegetation structure, supplying accurate data at high post-spacing and on a landscape-scale, which is impossible to achieve with field-based methods. We investigated how climate affected habitat quality using great tits (Parus major) breeding in woodland in eastern England. Mean chick body mass was used as a measure of habitat quality. Mean canopy height, calculated from a lidar digital canopy height model, was used as a measure of habitat structure. The influence of canopy height on body mass was examined for seven years during which weather conditions varied. The slopes and correlation coefficients of the mass/height relationships were related linearly to the warmth sum, an index of spring warmth, such that chick mass declined with canopy height in cold, late springs, but increased with height in warm, early springs. The parameters of the mass/height relationships, and the warmth sum, were also related linearly to the winter North Atlantic Oscillation index, but with a time lag of one year. Within the same wood, the structure conferring “best” habitat quality differed between years depending on weather conditions.

Separability of tropical rain-forest types in the Tambopata-Candamo Reserved Zone, Peru

Abstract The spectral separability of twelve tropical rain-forest classes was examined in Landsat Thematic Mapper (TM∥ imagery of the Tambopata-Candamo Reserved Zone, south-east Peru. Spatial filtering of the imagery increased inter-class separability, although spectral overlap between the twelve forest classes was such that only four broad forest groups could be separated. These four spectrally separable forest groups appeared to differ in terms of structure and crown characteristics.

Why are tropical rain forests so species rich? Classifying, reviewing and evaluating theories

Two classifications are presented that organize the major processes and theories addressing the high species diversity of tropical rain forests. The first typology organizes environmental and biological processes within a spatio-temporal hierarchy, whilst the second classifies 12 theories according to over-arching driving forces: genetic differentiation, environmental change, niche/habitat diversification and biotic interaction. The theories are reviewed and evaluated by delineating the development and current state of academic knowledge pertaining to each. General issues that arise from examining species diversity within the tropical realm are discussed and this indicates where the academic debate stands today. Some thoughts concerning future research avenues are included.