Measuring and monitoring linear woody features in agricultural landscapes through earth observation data as an indicator of habitat availability

AbstractThe loss of natural habitats and the loss of biological diversity is a global problem affecting all ecosystems including agricultural landscapes. Indicators of biodiversity can provide standardized measures that make it easier to compare and communicate changes to an ecosystem. In agricultural landscapes the amount and variety of available habitat is directly correlated with biodiversity levels. Linear woody features (LWF), including hedgerows, windbreaks, shelterbelts as well as woody shrubs along fields, roads and watercourses, play a vital role in supporting biodiversity as well as serving a wide variety of other purposes in the ecosystem. Earth observation can be used to quantify and monitor LWF across the landscape. While individual features can be manually mapped, this research focused on the development of methods using line intersect sampling (LIS) for estimating LWF as an indicator of habitat availability in agricultural landscapes. The methods are accurate, efficient, repeatable and provide robust results. Methods were tested over 9.5Mha of agricultural landscape in the Canadian Mixedwood Plains ecozone. Approximately 97,000km of LWF were estimated across this landscape with results useable both at a regional reporting scale, as well as mapped across space for use in wildlife habitat modelling or other landscape management research. The LIS approach developed here could be employed at a variety of scales in particular for large regions and could be adapted for use as a national scale indicator of habitat availability in heavily disturbed agricultural landscape

A Systematic Approach for Variable Selection With Random Forests: Achieving Stable Variable Importance Values

Random Forests variable importance measures are often used to rank variables by their relevance to a classification problem and subsequently reduce the number of model inputs in high-dimensional data sets, thus increasing computational efficiency. However, as a result of the way that training data and predictor variables are randomly selected for use in constructing each tree and splitting each node, it is also well known that if too few trees are generated, variable importance rankings tend to differ between model runs. In this letter, we characterize the effect of the number of trees (ntree) and class separability on the stability of variable importance rankings and develop a systematic approach to define the number of model runs and/or trees required to achieve stability in variable importance measures. Results demonstrate that both a large ntree for a single model run, or averaged values across multiple model runs with fewer trees, are sufficient for achieving stable mean importance values. While the latter is far more computationally efficient, both the methods tend to lead to the same ranking of variables. Moreover, the optimal number of model runs differs depending on the separability of classes. Recommendations are made to users regarding how to determine the number of model runs and/or trees that are required to achieve stable variable importance rankings

Development of a forest structural complexity index based on multispectral airborne remote sensing and topographic data

This paper presents development of a multivariate forest structural complexity index based on relationships between field-based structural variables and geospatial data. Remote sensing has been widely used to model individual forest structural attributes at many scales. As opposed to, or in addition to, individual structural parameters such as leaf area index or tree height, overall structural complexity information can enhance forest inventories and provide a variety of information to forest managers, including identifying damage and disturbance as well as indicators of habitat or biodiversity. In this study, a multivariate modelling technique, redundancy analysis, was implemented to derive a model incorporating both horizontal and vertical structural attributes as predicted by an ensemble of high-resolution multispectral airborne imagery and topographic variables. The first redundancy analysis axis of the final model explained 35% of the total variance of the field variables and was used as the complexity index. With a root mean squared error of 19.9%, the model was capable of differentiating four to five relative levels of complexity. This paper presents the forest ecological and modelling aspects of the research. A related paper presents the remote sensing aspects, including application of the model to map predicted structural complexity, map validation, and testing of the method at multiple scales

Terrestrial ecosystem monitoring in Canada and the greater role for integrated earth observation

Ecosystems are valuable as well as aesthetic. The natural functions of ecosystems can have profound effects on the economy, and human and wildlife health. The aggregate value of these âœecosystem servicesâ? may far exceed the economic value derived from resource extraction or industrial development, especially when considering the costs of restoring ecosystems. There is increasing interest, therefore, in monitoring and protecting ecosystems, and accounting for the biodiversity and services they provide. In 2010, Canada undertook a review of ecosystem status and trends that identified the regions and ecosystems where management is most urgently needed. The authors concluded that more large-scale, long-term, standardized, and spatially complete information is needed for effective monitoring and management. Satellite-based earth observation (EO) tools were seen as a means of addressing this information need. In a separate exercise, a list of priority questions for conservation policy and management at a national level was produced: the resolution of three-quarters of those questions appears to depend on EO tools to a significant or critical extent. Canada has a long and successful history in all aspects of earth observation, placing it amongst the leaders in the international remote sensing community. Whereas the need for measuring ecosystem services to humans and wildlife is increasingly important, the challenges for doing so are increasingly significant and the technology required is increasingly complex. Overcoming these challenges is necessary to address emerging conservation priorities including measurement of ecosystem attributes to support habitat conservation for Species at Risk, measuring functional capacity of ecosystems to mitigate effects of climate change, monitoring and mitigating effects of resource extraction, and supporting industrial development in Canadaâ™s north. Addressing emerging priorities requires dialogue among ecologists and decision makers, coordinated at regional and national scales, and requires drawing on the best EO technologies and infrastructure available. This review highlights the urgency of a coordinated approach for innovative applications of EO tools toward conservation and discusses some of the key elements that might be included and opportunities and challenges that might be encountered, by such an approach

Optimizing landscape selection for estimating relative effects of landscape variables on ecological responses

Empirical studies of the relative effects of landscape variables may compromise inferential strength with common approaches to landscape selection. We propose a methodology for landscape sample selection that is designed to overcome some common statistical pitfalls that may hamper estimates of relative effects of landscape variables on ecological responses. We illustrate our proposed methodology through an application aimed at quantifying the relationships between farmland heterogeneity and biodiversity. For this project, we required 100 study landscapes that represented the widest possible ranges of compositional and configurational farmland heterogeneity, where these two aspects of heterogeneity were quantified as crop cover diversity (Shannon diversity index) and mean crop field size, respectively. These were calculated at multiple spatial extents from a detailed map of the region derived through satellite image segmentation and classification. Potential study landscapes were then selected in a structured approach such that: (1) they represented the widest possible range of both heterogeneity variables, (2) they were not spatially autocorrelated, and (3) there was independence (no correlation) between the two heterogeneity variables, allowing for more precise estimates of the regression coefficients that reflect their independent effects. All selection criteria were satisfied at multiple extents surrounding the study landscapes, to allow for multi-scale analysis. Our approach to landscape selection should improve the inferential strength of studies estimating the relative effects of landscape variables, particularly those with a view to developing land management guidelines

The homogenizing influence of agriculture on forest bird communities at landscape scales

Context: Agricultural expansion is a principal driver of biodiversity loss, but the impacts on community assembly in agro-ecosystems are less clear, especially across regional scales at which agricultural policies are applied. Objectives: Using forest-breeding birds, we (1) tested whether increased agricultural coverage resulted in species communities that were random or more similar than expected, (2) compared the relative influence of agriculture versus distance in structuring communities, and (3) tested whether different responses to agriculture among functional guilds leads to a change in functional diversity across gradients of agriculture. Methods: Species abundances were sampled along 229 transects, each 8 km, using citizen science data assembled across a broad region of eastern Canada. Agricultural and natural land cover were each summed within three different-sized buffers (landscapes) around each transect. A null modeling approach was used to measure community similarity. Results: Communities were most similar between landscapes that had high agricultural coverage and became more dissimilar as their respective landscapes differed more strongly in the amount of agriculture. Community dissimilarity increased with geographic distance up to about 200 km. Dissimilarity with increasing agriculture was largely due to the disappearance of Neotropical migrants, insectivores and foliage-gleaners from the community as agriculture increas