Photomorphic analysis techniques: An interim spatial analysis using satellite remote sensor imagery and historical data

The use of machine scanning and/or computer-based techniques to provide greater objectivity in the photomorphic approach was investigated. Photomorphic analysis and its application in regional planning are discussed. Topics included: delineation of photomorphic regions; inadequacies of existing classification systems; tonal and textural characteristics and signature analysis techniques; pattern recognition and Fourier transform analysis; and optical experiments. A bibliography is included

Mapping and assessment of ecosystems and their services. Urban ecosystems

Action 5 of the EU Biodiversity Strategy to 2020 requires member states to Map and Assess the state of Ecosystems and their Services (MAES). This report provides guidance for mapping and assessment of urban ecosystems. The MAES urban pilot is a collaboration between the European Commission, the European Environment Agency, volunteering Member States and cities, and stakeholders. Its ultimate goal is to deliver a knowledge base for policy and management of urban ecosystems by analysing urban green infrastructure, condition of urban ecosystems and ecosystem services. This report presents guidance for mapping urban ecosystems and includes an indicator framework to assess the condition of urban ecosystems and urban ecosystem services. The scientific framework of mapping and assessment is designed to support in particular urban planning policy and policy on green infrastructure at urban, metropolitan and regional scales. The results are based on the following different sources of information: a literature survey of 54 scientific articles, an online-survey (on urban ecosystems, related policies and planning instruments and with participation of 42 cities), ten case studies (Portugal: Cascais, Oeiras, Lisbon; Italy: Padua, Trento, Rome; The Netherlands: Utrecht; Poland: Poznań; Spain: Barcelona; Norway: Oslo), and a two-day expert workshop. The case studies constituted the core of the MAES urban pilot. They provided real examples and applications of how mapping and assessment can be organized to support policy; on top, they provided the necessary expertise to select a set of final indicators for condition and ecosystem services. Urban ecosystems or cities are defined here as socio-ecological systems which are composed of green infrastructure and built infrastructure. Urban green infrastructure (GI) is understood in this report as the multi-functional network of urban green spaces situated within the boundary of the urban ecosystem. Urban green spaces are the structural components of urban GI. This study has shown that there is a large scope for urban ecosystem assessments. Firstly, urban policies increasingly use urban green infrastructure and nature-based solutions in their planning process. Secondly, an increasing amount of data at multiple spatial scales is becoming available to support these policies, to provide a baseline, and to compare or benchmark cities with respect to the extent and management of the urban ecosystem. Concrete examples are given on how to delineate urban ecosystems, how to choose an appropriate spatial scale, and how to map urban ecosystems based on a combination of national or European datasets (including Urban Atlas) and locally collected information (e.g., location of trees). Also examples of typologies for urban green spaces are presented. This report presents an indicator framework which is composed of indicators to assess for urban ecosystem condition and for urban ecosystem services. These are the result of a rigorous selection process and ensure consistent mapping and assessment across Europe. The MAES urban pilot will continue with work on the interface between research and policy. The framework presented in this report needs to be tested and validated across Europe, e.g. on its applicability at city scale, on how far the methodology for measuring ecosystem condition and ecosystem service delivery in urban areas can be used to assess urban green infrastructure and nature-based solutions

The Role of Landscape Connectivity in Planning and Implementing Conservation and Restoration Priorities. Issues in Ecology

Landscape connectivity, the extent to which a landscape facilitates the movements of organisms and their genes, faces critical threats from both fragmentation and habitat loss. Many conservation efforts focus on protecting and enhancing connectivity to offset the impacts of habitat loss and fragmentation on biodiversity conservation, and to increase the resilience of reserve networks to potential threats associated with climate change. Loss of connectivity can reduce the size and quality of available habitat, impede and disrupt movement (including dispersal) to new habitats, and affect seasonal migration patterns. These changes can lead, in turn, to detrimental effects for populations and species, including decreased carrying capacity, population declines, loss of genetic variation, and ultimately species extinction. Measuring and mapping connectivity is facilitated by a growing number of quantitative approaches that can integrate large amounts of information about organisms’ life histories, habitat quality, and other features essential to evaluating connectivity for a given population or species. However, identifying effective approaches for maintaining and restoring connectivity poses several challenges, and our understanding of how connectivity should be designed to mitigate the impacts of climate change is, as yet, in its infancy. Scientists and managers must confront and overcome several challenges inherent in evaluating and planning for connectivity, including: •characterizing the biology of focal species; •understanding the strengths and the limitations of the models used to evaluate connectivity; •considering spatial and temporal extent in connectivity planning; •using caution in extrapolating results outside of observed conditions; •considering non-linear relationships that can complicate assumed or expected ecological responses; •accounting and planning for anthropogenic change in the landscape; •using well-defined goals and objectives to drive the selection of methods used for evaluating and planning for connectivity; •and communicating to the general public in clear and meaningful language the importance of connectivity to improve awareness and strengthen policies for ensuring conservation. Several aspects of connectivity science deserve additional attention in order to improve the effectiveness of design and implementation. Research on species persistence, behavioral ecology, and community structure is needed to reduce the uncertainty associated with connectivity models. Evaluating and testing connectivity responses to climate change will be critical to achieving conservation goals in the face of the rapid changes that will confront many communities and ecosystems. All of these potential areas of advancement will fall short of conservation goals if we do not effectively incorporate human activities into connectivity planning. While this Issue identifies substantial uncertainties in mapping connectivity and evaluating resilience to climate change, it is also clear that integrating human and natural landscape conservation planning to enhance habitat connectivity is essential for biodiversity conservation

ADVANCING THE TERRESTRIAL ECOLOGICAL UNIT INVENTORY WITHIN THE WHITE MOUNTAIN NATIONAL FOREST USING LiDAR

Forest land managers need ecological classification to assess and describe resource conditions, vegetation conditions, outcomes resulting from various management prescription scenarios, and communicate environmental effects of land management planning alternatives. However, there is a need to incorporate more ecological classification into the land management plans. The U.S. Forest Service’s approach, the Terrestrial Ecological Unit Inventory (TEUI), relies heavily on field data collection and verification of map unit delineations that is time-consuming and costly. Traditional mapping methods far exceed the current financial capacity of the U.S. Forest Service. In order to justify new ecological classification mapping approaches, there needs to be significant evidence that new approaches will create equivalent or superior map products, reduce costs, improve efficiencies and maybe improve accuracy. Therefore the objectives of chapter 2 were to use the Soil Inference Engine (SIE) to partition the areal extent of a project area watershed in the White Mountain National Forest (WMNF) using on topographic metrics derived from Light Detection and Ranging (LiDAR) data including both timber managed and un-managed timber production lands. A total of 189 plots were randomly generated within strata, based on parent material, and topographic metrics using a stratified random sampling approach. The number of plots calculated for stratified random sampling was predominately determined by the number of strata, the acres of timber-managed areas, and budget. 172 of those plots had both vegetation and soils information recorded. The results from chapter 2 showed that stratified random sampling using LiDAR-derived topographic metrics as SIE data inputs were sufficient in capturing the environmental gradients required by the U.S. Forest Service ecological classification requirements. Additionally, 10 New Hampshire Natural sensitive indicator species were located and recorded in 16% of plots stratified by topographic metrics and parent material. These results suggest this new approach to ecological classification on the WMNF improved the accuracy and efficiency in delineating ecological areas as well as locating the presence of nutrient rich areas. The objectives of chapter 3 used nonmetric multidimensional scaling (NMDS) to assess the relationship between understory species and environmental variables, including parent material, slope, aspect, elevation, and wetness. The results from chapter 3 showed how both soil properties and topographic metrics correlated with understory species in ordination space. NMDS ordination explained 81.1% of the cumulative variation of understory species presence in three dimensions using soil properties and topographic metrics with a final stress value of 17.3 and a p-value of 0.04. NMDS results also suggested that understory species clustered distinctly within New Hampshire Natural Community types. These results also support the idea that LiDAR-derived topographic metrics could assist in determining where community types are positioned across a landscape. Additional NMDS analysis also showed either soil chemistry or topographic metrics explained nearly equal amounts of cumulative understory species variation. The results from this objective highlights the use of topographic metrics as predictors of understory vegetation, and likely community types, which could be validated in other WMNF watersheds. Finally, the primary challenge for ecological classification is reducing the cost of traditional unit mapping. Therefore, the objectives of chapter 4 was a conceptual synthesis of the reasoning behind doing ecological classification. Information from the WMNF management plans of 1985 and 2005, and current National and Regional land management direction of the US Forest Service were reviewed. A cost review of ecological classification by stratified random sampling using LiDAR-derived topographic metrics was compared to traditional TEUI mapping methods. In both approaches, the mapping of the plots averaged approximately $989.00 per plot including soil chemistry analysis from U.S. Forest Service Laboratory. This yielded a total cost of approximately$623,000 for the traditional TEUI compared to approximately $221,000 including the LiDAR acquisition required for stratified random sampling using topographic metrics. This chapter showed that stratified random sampling using LiDAR-derived topographic metrics reduced costs by approximately$402,000, including the additional LiDAR acquisition costs, compared to the traditional TEUI approach

An HGIS Approach to Land-Use/Land-Cover Change in the Blanice Watershed, Czech Republic

In the South Bohemian region of the Czech Republic, the landscape is distinguished by a network of long narrow fields bordered by hedgerows clustered in small groups. These unique clusters of hedgerows have been interacting with their environment, effectively mitigating erosion, since they were first established in the High Middle Ages. In this research project I used historical maps to characterize land-use and land-cover (LULC) change relating to hedgerow features in one cadastral territory in the Blanice Watershed. Using georeferenced historical maps from 1837 and 1952, and unreferenced historical maps from 1837 to 1953, I compared the historical LULC to the current LULC within the cadastral territory of Křišťanovice. From 1837 to present-day Křišťanovice, the percentage of farmed land has decreased from 59.9% to 25.8%, while the percentage of forested area has increased from 26.6% to 61.9%. These changes reflect historical trends in land management as well as the impact of social and political changes on the environment. This project is also a methodological and epistemological exploration of a Historical GIS approach to research, and the methods developed to conduct LULC change analysis reflect these theoretical components. The results of this research provide a spatiotemporal HGIS analysis of LULC change, a workflow for applying the HGIS methods developed for this research, and a geodatabase for the storage, classification, and visualization of historical LULC data

Report of the panel on the land surface: Process of change, section 5

The panel defined three main areas of study that are central to the Solid Earth Science (SES) program: climate interactions with the Earth's surface, tectonism as it affects the Earth's surface and climate, and human activities that modify the Earth's surface. Four foci of research are envisioned: process studies with an emphasis on modern processes in transitional areas; integrated studies with an emphasis on long term continental climate change; climate-tectonic interactions; and studies of human activities that modify the Earth's surface, with an emphasis on soil degradation. The panel concluded that there is a clear requirement for global coverage by high resolution stereoscopic images and a pressing need for global topographic data in support of studies of the land surface

Guidance for benthic habitat mapping: an aerial photographic approach

This document, Guidance for Benthic Habitat Mapping: An Aerial Photographic Approach, describes proven technology that can be applied in an operational manner by state-level scientists and resource managers. This information is based on the experience gained by NOAA Coastal Services Center staff and state-level cooperators in the production of a series of benthic habitat data sets in Delaware, Florida, Maine, Massachusetts, New York, Rhode Island, the Virgin Islands, and Washington, as well as during Center-sponsored workshops on coral remote sensing and seagrass and aquatic habitat assessment. (PDF contains 39 pages) The original benthic habitat document, NOAA Coastal Change Analysis Program (C-CAP): Guidance for Regional Implementation (Dobson et al.), was published by the Department of Commerce in 1995. That document summarized procedures that were to be used by scientists throughout the United States to develop consistent and reliable coastal land cover and benthic habitat information. Advances in technology and new methodologies for generating these data created the need for this updated report, which builds upon the foundation of its predecessor

Beyond similarity: A network approach for identifying and delimiting biogeographical regions

Biogeographical regions (geographically distinct assemblages of species and communities) constitute a cornerstone for ecology, biogeography, evolution and conservation biology. Species turnover measures are often used to quantify biodiversity patterns, but algorithms based on similarity and clustering are highly sensitive to common biases and intricacies of species distribution data. Here we apply a community detection approach from network theory that incorporates complex, higher order presence-absence patterns. We demonstrate the performance of the method by applying it to all amphibian species in the world (c. 6,100 species), all vascular plant species of the USA (c. 17,600), and a hypothetical dataset containing a zone of biotic transition. In comparison with current methods, our approach tackles the challenges posed by transition zones and succeeds in identifying a larger number of commonly recognised biogeographical regions. This method constitutes an important advance towards objective, data derived identification and delimitation of the world's biogeographical regions.Comment: 5 figures and 1 supporting figur