Trisalyn A. Nelson

Multitemporal remote sensing of landscape dynamics and pattern change: describing natural and anthropogenic trends

Science and reporting information needs for monitoring dynamics in land cover over time have prompted research, and made operational, a wide variety of change detection methods utilizing multiple dates of remotely sensed data. Change detection procedures based upon spectral values are common; however, landscape pattern analysis approaches which utilize spatial information inherent within imagery present opportunities for the generation of unique and ecologically important information. While the use of two images may provide the means to identify change, the use of more than two images for long-term monitoring affords the ability to identify a greater range of processes of landscape change, including rates and dynamics. The main objective of this review is to investigate and summarize the methods and applications of land cover spatial pattern analysis using three or more image dates. The potential and the limitations of landscape pattern indices are identified and discussed to inform application recommendations. The second objective of this review is to make recommendations, including appropriate landscape pattern indices, for the application of landscape pattern analysis of a long time series of remotely sensed data to a case study involving the mountain pine beetle in British Columbia, Canada. The review concludes with recommendations for future research.

A review of quantitative methods for movement data

The collection, visualization, and analysis of movement data is at the forefront of geographic information science research. Movement data are generally collected by recording an objects spatial location (e.g., XY coordinates) at discrete time intervals. Methods for extracting useful information, for example space–time patterns, from these increasingly large and detailed datasets have lagged behind the technology for generating them. In this article we review existing quantitative methods for analyzing movement data. The objective of this article is to provide a synthesis of the existing literature on quantitative analysis of movement data while identifying those techniques that have merit with novel datasets. Seven classes of methods are identified: (1) time geography, (2) path descriptors, (3) similarity indices, (4) pattern and cluster methods, (5) individual–group dynamics, (6) spatial field methods, and (7) spatial range methods. Challenges routinely faced in quantitative analysis of movement data include difficulties with handling space and time attributes together, representing time in GIS, and using classical statistical testing procedures with space–time movement data. Areas for future research include investigating equivalent distance comparisons in space and time, measuring interactions between moving objects, developing predictive frameworks for movement data, integrating movement data with existing geographic layers, and incorporating theory from time geography into movement models. In conclusion, quantitative analysis of movement data is an active research area with tremendous opportunity for new developments and methods.

Review of methods for space-time disease surveillance.

Abstract A review of some methods for analysis of space–time disease surveillance data is presented. Increasingly, surveillance systems are capturing spatial and temporal data on disease and health outcomes in a variety of public health contexts. A vast and growing suite of methods exists for detection of outbreaks and trends in surveillance data and the selection of appropriate methods in a given surveillance context is not always clear. While most reviews of methods focus on algorithm performance, in practice, a variety of factors determine what methods are appropriate for surveillance. In this review, we focus on the role of contextual factors such as scale, scope, surveillance objective, disease characteristics, and technical issues in relation to commonly used approaches to surveillance. Methods are classified as testing-based or model-based approaches. Reviewing methods in the context of factors other than algorithm performance highlights important aspects of implementing and selecting appropriate disease surveillance methods.

Mobile phone-based infectious disease surveillance system, Sri Lanka.

Because many infectious diseases are emerging in animals in low-income and middle-income countries, surveillance of animal health in these areas may be needed for forecasting disease risks to humans. We present an overview of a mobile phone–based frontline surveillance system developed and implemented in Sri Lanka. Field veterinarians reported animal health information by using mobile phones. Submissions increased steadily over 9 months, with ≈4,000 interactions between field veterinarians and reports on the animal population received by the system. Development of human resources and increased communication between local stakeholders (groups and persons whose actions are affected by emerging infectious diseases and animal health) were instrumental for successful implementation. The primary lesson learned was that mobile phone–based surveillance of animal populations is acceptable and feasible in lower-resource settings. However, any system implementation plan must consider the time needed to garner support for novel surveillance methods among users and stakeholders.

Comparison of airborne and satellite high spatial resolution data for the identification of individual trees with local maxima filtering

High spatial resolution airborne remotely sensed data have been considered a test bed for the utility of future satellite sensors. Techniques developed on airborne data are now being applied to high spatial resolution imagery collected from remote sensing satellites. In this Letter we compare the results of local maxima (LM) filtering for the identification of individual trees on a 1 m spatial resolution airborne Multi-detector Electro-optical Imaging Sensor II (MEIS II) image and a 1 m IKONOS image. With a relatively large spatial extent, comparative ease of acquisition, and radiometric consistency across the imagery, IKONOS 1 m spatial resolution data have potential utility for forestry applications. However, the results of the LM filtering indicate that although the IKONOS data accurately identify 85% of individual trees in the study area, the commission error is large (51%) and this error may be problematic for certain applications. This is compared to an overall accuracy of 67% for the MEIS II with a commission error of 22%. Further work in developing LM techniques for IKONOS data is required. These methods may be useful to forest stewards, who increasingly seek spatially explicit information on individual trees to serve as the foundation for more accurate modelling of forest structure and dynamics.

Review of software for space-time disease surveillance

Disease surveillance makes use of information technology at almost every stage of the process, from data collection and collation, through to analysis and dissemination. Automated data collection systems enable near-real time analysis of incoming data. This context places a heavy burden on software used for space-time surveillance. In this paper, we review software programs capable of space-time disease surveillance analysis, and outline some of their salient features, shortcomings, and usability. Programs with space-time methods were selected for inclusion, limiting our review to ClusterSeer, SaTScan, GeoSurveillance and the Surveillance package for R. We structure the review around stages of analysis: preprocessing, analysis, technical issues, and output. Simulated data were used to review each of the software packages. SaTScan was found to be the best equipped package for use in an automated surveillance system. ClusterSeer is more suited to data exploration, and learning about the different methods of statistical surveillance.

Potential contributions of remote sensing to ecosystem service assessments

Ecological and conservation research has provided a strong scientific underpinning to the modeling of ecosystem services (ESs) over space and time, by identifying the ecological processes and components of biodiversity (ecosystem service providers, functional traits) that drive ES supply. Despite this knowledge, efforts to map the distribution of ESs often rely on simple spatial surrogates that provide incomplete and non-mechanistic representations of the biophysical variables they are intended to proxy. However, alternative data sets are available that allow for more direct, spatially nuanced inputs to ES mapping efforts. Many spatially explicit, quantitative estimates of biophysical parameters are currently supported by remote sensing, with great relevance to ES mapping. Additional parameters that are not amenable to direct detection by remote sensing may be indirectly modeled with spatial environmental data layers. We review the capabilities of modern remote sensing for describing biodiversity, plant traits, vegetation condition, ecological processes, soil properties, and hydrological variables and highlight how these products may contribute to ES assessments. Because these products often provide more direct estimates of the ecological properties controlling ESs than the spatial proxies currently in use, they can support greater mechanistic realism in models of ESs. By drawing on the increasing range of remote sensing instruments and measurements, data sets appropriate to the estimation of a given ES can be selected or developed. In so doing, we anticipate rapid progress to the spatial characterization of ecosystem services, in turn supporting ecological conservation, management, and integrated land use planning.

Issues in species classification of trees in old growth conifer stands

Old growth temperate conifer forest canopies are composed of assemblages of tree crowns that vary by species, height, size, and intercrown distance. The challenge this complexity presents to species classification is formidable. In this paper we describe the exploration of spectral properties of old growth tree crowns as captured on two independent acquisitions of 0.7 m ground resolution compact airborne spectrographic imager (CASI) airborne multispectral imagery. Underlying spectral separability is examined, and classifications of manually delineated crowns are compared against field-surveyed ground truth. Technical issues and solutions addressing individual tree species classification of old growth conifer stands are discussed. The study site is a western hemlock, amabilis fir, and red cedar dominated old growth area on Vancouver Island on the west coast of Canada. Within-species spectral variability is large because of illumination and view-angle conditions, openness of trees, natural variability, shadowing effects, and a range of crown health. As well, spectral differences between species are not large. An object-oriented illumination and view-angle correction was effective at reducing the effect of view angle on spectral variability. Radiometric normalization between imagery from flight lines of the same site and time period was successful, but normalization with data from other sites and days was not. The use of spectral samples from sunlit areas of the tree crowns (mean-lit signature) produced the best spectral separability and species classification. Because of the wide within-species variability and spectral overlap among species, it was also found useful to create internal subclasses within a species (e.g., normal and bright crowns). It was not feasible to consistently classify species of shaded crowns or stressed trees, and it was necessary to create overall shaded tree and unhealthy classes to prevent these trees from corrupting the final species classification. Classification results also depend on the visibility of trees in the imagery. This was demonstrated by different visibility and shade conditions of trees between the two image dates. This effect is particularly strong in old growth stands because of variations in stem density and spacing and the range of tree heights and sizes. Old growth stands will have shaded, unhealthy, and visually or spectrally unusual trees. Excluding these and considering species classification of manually delineated trees of the normal and bright spectral classes, modest success was achieved, in the order of 78% accuracy. Hemlock and balsam were confused, and cedar classification was mostly confused by the presence of unhealthy trees. If speciation of shaded and unhealthy trees was required, overall species classification was weak. It was shown, however, that shaded and unhealthy trees could be identified well using a classification with shaded and unhealthy classes. Species classification of the remaining trees, including the unusual trees, was 67% for the 1996 imagery and 77% for the 1998 imagery. Despite the difficulties in classifying species in an old growth environment, practical solutions to issues are available and viable spectral classifications are possible. Fully automated species isolation and classification add further complications, however, and new approaches beyond simple spectral techniques need to be explored.

Spatial data, analysis approaches, and information needs for spatial ecosystem service assessments: a review

Operational use of the ecosystem service (ES) concept in conservation and planning requires quantitative assessments based on accurate mapping of ESs. Our goal is to review spatial assessments of ESs, with an emphasis on the socioecological drivers of ESs, the spatial datasets commonly used to represent those drivers, and the methodological approaches used to spatially model ESs. We conclude that diverse strategies, integrating both spatial and aspatial data, have been used to map ES supply and human demand. Model parameters representing abiotic ecosystem properties can be supported by use of well-developed and widely available spatial datasets. Land-cover data, often manipulated or subject to modeling in a GIS, is the most common input for ES modeling; however, assessments are increasingly informed by a mechanistic understanding of the relationships between drivers and services. We suggest that ES assessments are potentially weakened by the simplifying assumptions often needed to translate between conceptual models and widely used spatial data. Adoption of quantitative spatial data that more directly represent ecosystem properties may improve parameterization of mechanistic ES models and increase confidence in ES assessments.

Regionalization of Landscape Pattern Indices Using Multivariate Cluster Analysis

Regionalization, or the grouping of objects in space, is a useful tool for organizing, visualizing, and synthesizing the information contained in multivariate spatial data. Landscape pattern indices can be used to quantify the spatial pattern (composition and configuration) of land cover features. Observable patterns can be linked to underlying processes affecting the generation of landscape patterns (e.g., forest harvesting). The objective of this research is to develop an approach for investigating the spatial distribution of forest pattern across a study area where forest harvesting, other anthropogenic activities, and topography, are all influencing forest pattern. We generate spatial pattern regions (SPR) that describe forest pattern with a regionalization approach. Analysis is performed using a 2006 land cover dataset covering the Prince George and Quesnel Forest Districts, 5.5 million ha of primarily forested land base situated within the interior plateau of British Columbia, Canada. Multivariate cluster analysis (with the CLARA algorithm) is used to group landscape objects containing forest pattern information into SPR. Of the six generated SPR, the second cluster (SPR2) is the most prevalent covering 22% of the study area. On average, landscapes in SPR2 are comprised of 55.5% forest cover, and contain the highest number of patches, and forest/non-forest joins, indicating highly fragmented landscapes. Regionalization of landscape pattern metrics provides a useful approach for examining the spatial distribution of forest pattern. Where forest patterns are associated with positive or negative environmental conditions, SPR can be used to identify similar regions for conservation or management activities.