Payments for Ecosystem Services: Mechanisms to Achieve Desired Landscape Patterns

This paper evaluates the effectiveness of five payment for ecosystem service (PES) schemes at meeting conservation objectives when the spatial configuration is important in meeting desired landscape patterns. The five PES schemes are: 1) fee-simple acquisition; 2) subsidies; 3) tradable development rights (TDR) with zoning; 4) mitigation banking; and 5) purchased development rights (PDR) easements. Findings are that tradeoffs exist between PES schemes for meeting spatial conservation objectives. The appropriate PES scheme incentive mechanism for a given region will depend upon economic demand as well as the landowner and landscape characteristics of the conservation region.Landscape, Spatial conservation, payment for ecosystem services, PES

Landscape patterns and forest island interactions

On March 18, the Field Station welcomed faculty and graduate students involved in the Landscape Pattern Analysis project. This study has both theoretical and practical objectives. The theoretical problems are concerned with island biogeography as outlined by Robert MacArthur and E. O. Wilson were discussed in the Spring 1975 Field Station Bulletin. These questions relate principally to how island size and distance from neighboring islands affect species composition and extinction, i.e., affect the diversity of island communities. These theoretical questions are being approached in a series of studies undertaken jointly by scientists at the Oak Ridge National Laboratory, Rutgers University, The University of Wisconsin-Milwaukee and the University of Wisconsin-Madison

Cybergis-enabled remote sensing data analytics for deep learning of landscape patterns and dynamics

Mapping landscape patterns and dynamics is essential to various scientific domains and many practical applications. The availability of large-scale and high-resolution light detection and ranging (LiDAR) remote sensing data provides tremendous opportunities to unveil complex landscape patterns and better understand landscape dynamics from a 3D perspective. LiDAR data have been applied to diverse remote sensing applications where large-scale landscape mapping is among the most important topics. While researchers have used LiDAR for understanding landscape patterns and dynamics in many fields, to fully reap the benefits and potential of LiDAR is increasingly dependent on advanced cyberGIS and deep learning approaches. In this context, the central goal of this dissertation is to develop a suite of innovative cyberGIS-enabled deep-learning frameworks for combining LiDAR and optical remote sensing data to analyze landscape patterns and dynamics with four interrelated studies. The first study demonstrates a high-accuracy land-cover mapping method by integrating 3D information from LiDAR with multi-temporal remote sensing data using a 3D deep-learning model. The second study combines a point-based classification algorithm and an object-oriented change detection strategy for urban building change detection using deep learning. The third study develops a deep learning model for accurate hydrological streamline detection using LiDAR, which has paved a new way of harnessing LiDAR data to map landscape patterns and dynamics at unprecedented computational and spatiotemporal scales. The fourth study resolves computational challenges in handling remote sensing big data and deep learning of landscape feature extraction and classification through a cutting-edge cyberGIS approach

Valuing Natural Space and Landscape Fragmentation in Richmond, VA

Hedonic pricing methods and GIS (Geographic Information Systems) were used to evaluate relationships between sale price of single family homes and landscape fragmentation and natural land cover. Spatial regression analyses found that sale prices increase as landscapes become less fragmented and the amount of natural land cover around a home increases. The projected growth in population and employment in the Richmond, Virginia region and subsequent increases in land development and landscape fragmentation presents a challenge to sustaining intact healthy ecosystems in the Richmond region. Spatial regression analyses helped illuminate how land cover patterns influence sale prices and landscape patterns that are economically and ecologically advantageous

Landscape patterns of periphyton in the Florida Everglades

Periphyton is an abundant and ubiquitous feature of the Florida Everglades, often forming thick mats that blanket shallow sediments and submersed plants. They are considered to be primary ecosystem engineers in the Everglades by forming and stabilizing soils, controlling concentrations of nutrients and gases, and supplying food and structure for other organisms. Distribution patterns are related to underlying physicochemical gradients as well as those hydrologic changes imposed by water management. Because communities respond rapidly to environmental change, their use has been advocated to provide indication of system degradation or restoration. The authors review studies on the distribution of periphyton in the Everglades, highlighting major findings relevant to water management, and also areas where additional exploration is necessary

A stochastic model for landscape patterns of biodiversity

Many factors have been proposed to affect biodiversity patterns across landscapes, including patch area, patch isolation, edge distances, and matrix quality, but existing models emphasize only one or two of these factors at a time. Here we introduce a synthetic but simple individual-based model that generates realistic patterns of species richness and density as a function of landscape structure. In this model, we simulated the stochastic placement of home ranges in landscapes, thus combining features of existing random placement and mid-domain effect models. As such, the model allows investigation of whether and how geometric constraints on home range placement of individuals scale up to affect species abundance and richness in landscapes. The model encompassed a gradient of possible landscapes, from a strictly homogeneous landscape to an archipelago of discrete patches. The model incorporated only variation in home range size of individuals of different species, with a simple suitability index that controlled home range spread into areas of habitat and areas of inter-habitat matrix. Demographic details of birth, death, and migration, as well as effects of species interactions were not included. Nevertheless, this simple model generated realistic patterns of biodiversity, including species-area curves and increases in diversity and abundance with decreasing isolation and increasing distance from patch edges. Species-area slopes (z values) generated by the model fell within the range observed in empirical studies on both true islands and habitat patches. Isolation and edge effects were stronger when the matrix was unsuitable, and became progressively weaker as matrix suitability increased, again in accordance with many empirical studies. When applied to a real data set on the abundance of 20 small mammal species sampled in forest patches in the Atlantic Forest of Brazil, the model predicted increases in abundance and richness with increasing patch size, consistent with the general pattern observed with field data. The ability of this simple model to reproduce realistic qualitative patterns of biodiversity suggests that such patterns may be driven, at least in part, by geometric constraints acting on the placement of individual ranges, which ultimately affect biodiversity patterns at the landscape level

Inverse box-counting method: A fractal-based procedure to create biospheric landscape patterns

1st WSEAS International Conference on Landscape Architecture (LA 2008), Univ Algarve, Algarve, PORTUGAL, JUN 11-13, 2008International audiencePlanners and designers are interested in replicating biospheric landscape patterns to reclaim surface mines to match existing natural landscape patterns. One approach that shows promise is the use of fractal geometry to generate biospheric landscape patterns. While the measurement of the actual fractal dimension of a landscape can be difficult, a box-counting method was developed at INHP, Angers, France which approximates the spatial patterns of biospheric landscapes. Essentially the procedure entails covering a natural object/pattern with a regular grid of size r and then one simply counts the number of grid boxes, N(r), that contain some part of the object. The boxes are subdivided and the value of r is progressively reduced and N(r) is similarly re-measured until some of the boxes become empty (containing no landscape objects of interest). Then the fractal dimension of the object is approximated to be the log(N(r))/Iog(l/r). We illustrate this procedure by measuring and replicating a stand of trees in the Upper Peninsula of Michigan. Our study revealed a fractal number of 1.0 17 (p < 0.01), with a mean of 77.4 trees per 100 in by 100 in stand, and a standard deviation of 34.87 trees per stand

Habitat structure: a fundamental concept and framework for urban soil ecology

Habitat structure is defined as the composition and arrangement of physical matter at a location. Although habitat structure is the physical template underlying ecological patterns and processes, the concept is relatively unappreciated and underdeveloped in ecology. However, it provides a fundamental concept for urban ecology because human activities in urban ecosystems are often targeted toward management of habitat structure. In addition, the concept emphasizes the fine-scale, on-the-ground perspective needed in the study of urban soil ecology. To illustrate this, urban soil ecology research is summarized from the perspective of habitat structure effects. Among the key conclusions emerging from the literature review are: (1) habitat structure provides a unifying theme for multivariate research about urban soil ecology; (2) heterogeneous urban habitat structures influence soil ecological variables in different ways; (3) more research is needed to understand relationships among sociological variables, habitat structure patterns and urban soil ecology. To stimulate urban soil ecology research, a conceptual framework is presented to show the direct and indirect relationships among habitat structure and ecological variables. Because habitat structure serves as a physical link between sociocultural and ecological systems, it can be used as a focus for interdisciplinary and applied research (e.g., pest management) about the multiple, interactive effects of urbanization on the ecology of soils

The relative dependence of Spanish landscape pattern on environmental and geographical variables over time

The analysis of the dependence of landscape patterns on environment was carried out in order to investigate the landscape structure evolution of Spain. The underlying concept was that the dependence between landscape spatial structure and environmental factors could be gradually decreasing over time. Land cover data were recorded from aerial photo interpretation of 206 4 x 4 km(2) samples from three different years: 1956, 1984 and 1998. Geographical variables were taken into consideration together with the purely environmental ones. General Linear Models of repeated measures were then used to segregate environmental from geographical effects on the pattern of the land cover patches of the samples. Aridity, lithology and topography were the environmental factors used to analyse structural indices of landscape. Landscape composition has a higher dependence on environment than configuration. Environmental variables showed higher correlations with landscape composition and configuration than geographical variables. Ail-long them, overall the climatic aridity and topography significantly accounted for more variation than did lithology. There was a high degree of stability in land cover composition over time, with some significant exceptions. Nevertheless, the registered increase of fragmentation over time has demonstrated that configuration measures are needed to fully assess landscape change