Understanding the Drivers of Forest, Residential, and Agricultural Land Values in Yamhill County Using Hedonic Models

Hedonic modeling is commonly used in land and property value estimations in an attempt to identify the impact that various attributes have on the market value of that property. The purpose of this study is to examine the factors contributing to land value of agricultural, forest, and residential properties in Yamhill County, as part of the Spatial Ecosystem Services Analysis, Modeling, and Evaluation (SESAME, http://www.pdx.edu/ecosystem-services/) project. This paper discusses the process and preliminary results of the development of hedonic models that will be utilized for predicting land value changes under future land conversion scenarios. Applying the models to future scenarios will provide insight into the effect that land conversion will have on market value of land in Yamhill County, in order to elucidate one component of the total land value in the area. Numerous studies have performed hedonic modeling in order to provide greater understanding of the non-market ecosystem service values that are contributing to land values, and it is necessary to have baseline information on the value of environmental attributes in order to identify potential policy and planning activities that can preserve these values. Current methods for assessing the value of non-market ecosystem services are mostly in development stages, with few widely-accepted approaches. Utilizing hedonic modeling and other revealed preference techniques may provide valuable insight into the contribution of nonmarket goods and services, in order to ensure they are adequately accounted for in planning and management decisions

Ecosystem Services: Challenges and Opportunities for Hydrologic Modeling to Support Decision Making

Ecosystem characteristics and processes provide significant value to human health and well- being, and there is growing interest in quantifying those values. Of particular interest are water-related eco- system services and the incorporation of their value into local and regional decision making. This presents multiple challenges and opportunities to the hydrologic-modeling community. To motivate advances in water-resources research, we first present three common decision contexts that draw upon an ecosystem- service framework: scenario analysis, payments for watershed services, and spatial planning. Within these contexts, we highlight the particular challenges to hydrologic modeling, and then present a set of opportu- nities that arise from ecosystem-service decisions. The paper concludes with a set of recommendations regarding how we can prioritize our work to support decisions based on ecosystem-service valuation

SPATIAL MODELING OF THE THREAT OF DAMAGE TO THE PEATLAND ECOSYSTEM IN THE MAINLAND OF BENGKALIS REGENCY, RIAU PROVINCE

Peatlands are the stretch of ecosystem landscape with unique characteristics, both physically, chemically, and biodiversity. Anthropogenic activities in peatland use and disasters pose a threat to the preservation of the peatland ecosystem, which has impacts toward abiotic to the element of biodiversity (biotic). The purpose of this research is to model how the threat of the peatland ecosystem by using spatial data modeling. The method in this research using cloud-based GIS data analysis from Google earth engine, modeling distance parameter to variable modeling of interaction among landscapes on the peatland, and weight sum the value over raster-based spatial layer to determinate the thereat in the peatland ecosystem. The results of this study found zones where hot spots often occur. Modeling with euclidean distance to all modeling variables (except temperature) gives a clear effect on how the threats from each landscape interact with each other. We found that the threat of peatland damage in the high threat class dominates the plantation area reaching 30.9% of the total peatland area, whereas the forest landscape only has a high threat with a percentage of 1.9% and a low threat which the ecosystem is stable and natural reaching over 34.7 %. From this model, we succeeded in bringing up the idea to determine the priority area for policies where need to be done in handling the protection of peatland ecosystems, especially in plantations where the highest percentage of the ecosystem threat is in the high level with integrated peatland management. Keywords: Peatland ecosystem, landscape, threa

Assessment of ecosystem integrity of lowland dipterocarp forest ecosystem using remote sensing

Ecosystem Integrity Index (EII) is a concept to determine the quality or the health of an ecosystem. The EII development can assist forest managers and decision makers in the conservation effort and forest management in Malaysia through the development of a simple and easy-to-adopt index. The aim of this study is to assess and evaluate the EII through the development of forest structure empirical models from remotely sensed data for lowland dipterocarp forest in Malaysia. The objectives of this study are: (i) to assess the structure and composition of lowland dipterocarp forest in Malaysia, (ii) to develop empirical model for estimating stand structure from remotely sensed data, and (iii) to derive the ecosystem integrity index for lowland dipterocarp forest. Tree Basal Area (BA), aboveground biomass (AGB) and volume plot from plot data were used as dependent variables, while remote sensing data from Landsat, Pleiades and LiDAR were used as independent variables for model development. Tree plot census was carried out from 17 to 19 May 2016, while remote sensing data acquisition dates for Landsat, Pleiades and LiDAR were 13 March 2016, 24 January 2015 and April 2015 respectively. Forest Structure Modeling was carried out by means of a correlation analysis with the calibration of dependent and independent data to select the most significant and accurate remote sensing variables to derive empiric equation (model), fitting stage to select the best model with the highest coefficient of determination (R2) and the lowest root mean square error ( RMSE) validation of the final selected. The Ecosystem Integrity Index was developed by the average percentage of the predicted BA, AGB and model volume. The EII was categorised at five integrity levels as high (81–100%), medium high (61–80%), moderate (41–60%), medium low (21–40%) and low (0–20%). A total of 1035 trees with diameter at breast height (DBH) of 5.0 cm and above were recorded in 69.115 ha sampling areas. The total trees recorded represented 150 species from 87 genera and 34 families. Shorea macroptera (Dipterocarpaceae), S. leprosula (Dipterocarpaceae) and S. parviflora (Dipterocarpaceae) are three dominant species, with Species Important Value Index (SIVi) of 6.49%, 6.23% and 5.51%, respectively. Dipterocarpaceae is the most dominant with Family Important Value Index (FIVi) of 33.54%. The developed final model is robust and consistent with high R2 with range of 0.84 to 0.87. The final models constructed for AGB, BA and volume value of R2 are 0.85, 0.84 and 0.87 respectively. The RMSE of AGB, BA and volume model are 53.1 Mg/ha, 3.54 m2/ha and 46.4 m3/ha, respectively. The overall stand AGB, BA and volume for Sungai Menyala Forest Reserve is 282.29 Mg/ha, 17.68 m2/ha and 239.51 m3/ha. An Ecosystem Integrity Index (EII) assessment has been successfully demonstrated by this study with production of practical, multi-scaled, flexible, adjustable and policy-relevant index. The overall EII of Sungai Menyala Forest Reserve is in Category 3, which shows that the area is within the medium value

Science Tools to Implement Ecosystem Based Management in Massachusetts (DRAFT)

In this report we provide a framework for implementing ecosystem based management (EBM) and suggest a range of science information tools and their appropriate application to the decision making process. These tools can be broadly classified as modeling tools, decision analysis tools, and indicators. Modeling tools allow the user to organize data, communicate scientific findings to management and stakeholder audiences, and test alternative management scenarios. When used unwisely, however, models can preclude options, present unusable scenarios, generate results in scales that differ from management needs, and impose huge time, data, and technical requirements (Manno et al., 2008). Decision analysis tools can inform management decisions but should not be relied upon solely; they are valuable aids in the process and provide opportunities for all-stakeholder input, visualization, and scenario analysis. Indicators are scientific measurements of ecological or socio-economic phenomena that provide data for monitoring and evaluating the systems being managed. While indicators are by and large widely accepted, their selection is based on expert opinion and involves a level of subjectivity. Inappropriately selected indicators can misinform management decisions. By helping to identify and mitigate lack of information, these science tools can be of great value in the shift to ecosystem based management

EcoGIS – GIS tools for ecosystem approaches to fisheries management

Executive Summary: The EcoGIS project was launched in September 2004 to investigate how Geographic Information Systems (GIS), marine data, and custom analysis tools can better enable fisheries scientists and managers to adopt Ecosystem Approaches to Fisheries Management (EAFM). EcoGIS is a collaborative effort between NOAA’s National Ocean Service (NOS) and National Marine Fisheries Service (NMFS), and four regional Fishery Management Councils. The project has focused on four priority areas: Fishing Catch and Effort Analysis, Area Characterization, Bycatch Analysis, and Habitat Interactions. Of these four functional areas, the project team first focused on developing a working prototype for catch and effort analysis: the Fishery Mapper Tool. This ArcGIS extension creates time-and-area summarized maps of fishing catch and effort from logbook, observer, or fishery-independent survey data sets. Source data may come from Oracle, Microsoft Access, or other file formats. Feedback from beta-testers of the Fishery Mapper was used to debug the prototype, enhance performance, and add features. This report describes the four priority functional areas, the development of the Fishery Mapper tool, and several themes that emerged through the parallel evolution of the EcoGIS project, the concept and implementation of the broader field of Ecosystem Approaches to Management (EAM), data management practices, and other EAM toolsets. In addition, a set of six succinct recommendations are proposed on page 29. One major conclusion from this work is that there is no single “super-tool” to enable Ecosystem Approaches to Management; as such, tools should be developed for specific purposes with attention given to interoperability and automation. Future work should be coordinated with other GIS development projects in order to provide “value added” and minimize duplication of efforts. In addition to custom tools, the development of cross-cutting Regional Ecosystem Spatial Databases will enable access to quality data to support the analyses required by EAM. GIS tools will be useful in developing Integrated Ecosystem Assessments (IEAs) and providing pre- and post-processing capabilities for spatially-explicit ecosystem models. Continued funding will enable the EcoGIS project to develop GIS tools that are immediately applicable to today’s needs. These tools will enable simplified and efficient data query, the ability to visualize data over time, and ways to synthesize multidimensional data from diverse sources. These capabilities will provide new information for analyzing issues from an ecosystem perspective, which will ultimately result in better understanding of fisheries and better support for decision-making. (PDF file contains 45 pages.

Parameterization and Sensitivity Analysis of the BIOME-BGC Terrestrial Ecosystem model: Net Primary Production Controls

Ecosystem simulation models use descriptive input parameters to establish the physiology, biochemistry, structure, and allocation patterns of vegetation functional types, or biomes. For single-stand simulations it is possible to measure required data, but as spatial resolution increases, so too does data unavailability. Generalized biome parameterizations are then required. Undocumented parameter selection and unknown model sensitivity to parameter variation for larger-resolution simulations are currently the major limitations to global and regional modeling. The authors present documented input parameters for a process-based ecosystem simulation model, BIOME–BGC, for major natural temperate biomes. Parameter groups include the following: turnover and mortality; allocation; carbon to nitrogen ratios (C:N); the percent of plant material in labile, cellulose, and lignin pools; leaf morphology; leaf conductance rates and limitations; canopy water interception and light extinction; and the percent of leaf nitrogen in Rubisco (ribulose bisphosphate-1,5-carboxylase/oxygenase) (PLNR). Using climatic and site description data from the Vegetation/Ecosystem Modeling and Analysis Project, the sensitivity of predicted annual net primary production (NPP) to variations in parameter level of ± 20% of the mean value was tested. For parameters exhibiting a strong control on NPP, a factorial analysis was conducted to test for interaction effects. All biomes were affected by variation in leaf and fine root C:N. Woody biomes were additionally strongly controlled by PLNR, maximum stomatal conductance, and specific leaf area while nonwoody biomes were sensitive to fire mortality and litter quality. None of the critical parameters demonstrated strong interaction effects. An alternative parameterization scheme is presented to better represent the spatial variability in several of these critical parameters. Patterns of general ecological function drawn from the sensitivity analysis are discussed

Dimethylsulphide, clouds, and phytoplankton: Insights from a simple plankton ecosystem feedback model

The hypothesis that marine plankton ecosystems may effectively regulate climate by the production of dimethylsulphide (DMS) has attracted substantial research effort over recent years. This hypothesis suggests that DMS produced by marine ecosystems can affect cloud properties and hence the averaged irradiance experienced by the phytoplankton that produce DMS’s precursor dimethylsulphoniopropionate (DMSP). This paper describes the use of a simple model to examine the effects of such a biogenic feedback on the ecosystem that initiates it. We compare the responses to perturbation of a simple marine nitrogen-phytoplankton-zooplankton (NPZ) ecosystem model with and without biogenic feedback. Our analysis of this heuristic model reveals that the addition of the feedback can increase the model’s resilience to perturbation and hence stabilize the model ecosystem. This result suggests the hypothesis that DMS may play a role in stabilizing marine plankton ecosystem dynamics through its effect on the atmosphere

Statistical uncertainty of eddy flux–based estimates of gross ecosystem carbon exchange at Howland Forest, Maine

We present an uncertainty analysis of gross ecosystem carbon exchange (GEE) estimates derived from 7 years of continuous eddy covariance measurements of forest-atmosphere CO2fluxes at Howland Forest, Maine, USA. These data, which have high temporal resolution, can be used to validate process modeling analyses, remote sensing assessments, and field surveys. However, separation of tower-based net ecosystem exchange (NEE) into its components (respiration losses and photosynthetic uptake) requires at least one application of a model, which is usually a regression model fitted to nighttime data and extrapolated for all daytime intervals. In addition, the existence of a significant amount of missing data in eddy flux time series requires a model for daytime NEE as well. Statistical approaches for analytically specifying prediction intervals associated with a regression require, among other things, constant variance of the data, normally distributed residuals, and linearizable regression models. Because the NEE data do not conform to these criteria, we used a Monte Carlo approach (bootstrapping) to quantify the statistical uncertainty of GEE estimates and present this uncertainty in the form of 90% prediction limits. We explore two examples of regression models for modeling respiration and daytime NEE: (1) a simple, physiologically based model from the literature and (2) a nonlinear regression model based on an artificial neural network. We find that uncertainty at the half-hourly timescale is generally on the order of the observations themselves (i.e., ∼100%) but is much less at annual timescales (∼10%). On the other hand, this small absolute uncertainty is commensurate with the interannual variability in estimated GEE. The largest uncertainty is associated with choice of model type, which raises basic questions about the relative roles of models and data