![](media/image1.wmf){width="1.625in" height="0.5986111111111111in"}

**GRA Prospectus Project:**

**Optimizing Design and Management of Protected Areas**

By Richard Bernknopf and David Halsing

####### Open-File Report 01-404

**2001**

Any use of trade, product, or firm names is for descriptive purposes
only and does not imply endorsement by the U.S. Government.

**U.S. Department of the Interior**

######## U.S. Geological Survey

# Review of Project Description

Protected areas comprise one major type of global conservation effort
that has been in the form of parks, easements, or conservation
concessions. Though protected areas are increasing in number and size
throughout tropical ecosystems, there is no systematic method for
optimally targeting specific local areas for protection, designing the
protected area, and monitoring it, or for guiding follow-up actions to
manage it or its surroundings over the long run. Without such a system,
conservation projects often cost more than necessary and/or risk
protecting ecosystems and biodiversity less efficiently than desired.
Correcting these failures requires tools and strategies for improving
the placement, design, and long-term management of protected areas. The
objective of this project is to develop a set of spatially based
analytical tools to improve the selection, design, and management of
protected areas.

In this project, several conservation concessions will be compared using
an economic optimization technique. The forest land use portfolio model
is an integrated assessment that measures investment in different land
uses in a forest. The case studies of individual tropical ecosystems are
developed as forest (land) use and preservation portfolios in a
geographic information system (GIS). Conservation concessions involve a
private organization purchasing development and resource access rights
in a certain area and retiring them. Forests are put into conservation,
and those people who would otherwise have benefited from extracting
resources or selling the right to do so are compensated. Concessions are
legal agreements wherein the exact amount and nature of the compensation
result from a negotiated agreement between an agent of the conservation
community and the local community. Funds are placed in a trust fund, and
annual payments are made to local communities and regional/national
governments. The payments are made pending third-party verification that
the forest expanse and quality have been maintained.

## RESEARCH PARTNERS

Recently Conservation International (CI) has established conservation
concessions as a means to conserve biodiversity. CI accepts private
donations and "invests" them in conservation concessions. The Field
Museum of Natural History (Field Museum) has a research branch that
conducts ecological classifications and rapid biological assessments.
The Field Museum has been working with CI to prioritize areas for
protection on the basis of ecological significance of habitat areas that
contain large quantities of biodiversity. CI combines economics,
finance, and negotiation with the Field Museum's biological and
ecological inventories to target areas for protection. Hardner and
Gullison Associates, LLC (HGA) is an environmental consulting firm that
assists CI with economic and ecological analyses and with negotiations
for each concession. Finally, the Ecosystem Science and Technology (EST)
Branch of the National Aeronautics and Space Administration (NASA)
provides expertise in remotely sensed instruments and information and in
the analysis of remotely sensed images in tropical environments.

At present, the first payment for the first concession has been made to
the National Forestry Commission in Guyana. Several other project
sites---in Perú, Bolivia, and Guatemala---are in different stages of the
assessment and decision process. These four sites are the case studies
for the USGS research project.

The Center for Applied Biodiversity Science (CABS) at CI, which has
completed work in biodiversity measurement and protection
prioritization, has developed large databases of remotely sensed and
field-based data. Much of this information is in a GIS that is suitable
for spatial analysis---a specialty of the USGS--National Mapping
Division's Center for Science Policy (CSP).

## Research Goals

Together with CI--CABS and the other research partners, the CSP is
developing an analytical tool to compare and rank protected areas for
forest selection, design, and management. The economic framework will
maximize conservation per dollar spent given a biodiversity standard or
target. Included in the model is an assessment of three types of risk:
misallocation of resources in the acquisition or protection of
ecosystems, the physical chance of loss or negative change to the
protected area, and the uncertainty around projections relating to
threats and opportunities for conservation in a given area.

The forest land use portfolio model is an application of the expected
utility model and assumes a state preference approach. It incorporates
risk aversion into the decision framework. This type of economic model
has not been applied to biodiversity and conservation problems. The
model minimizes the difference between the expected payoff of a
concession and the uncertainty associated with that payoff. This is an
effective way to compare unlike resources in different parts of the
world. Since funding for conservation concessions comes from donations
made by private citizens, it is important to demonstrate that those
donations secure the best use of tropical ecosystems.

## Research Hypotheses/Questions

The following questions are the core for a set of testable hypotheses
that will be evaluated over the life of the project:

-   What are the most cost-effective types and scales of remote sensing
    information to use in protected areas?

-   Can the goals of protected areas be met more effectively through
    increased and/or improved use of GIS, remote sensing, risk analysis,
    and economic models?

-   How can spatially based risk management and economic theory be
    applied to protected area creation, design, and management?

-   How can the forest land use portfolio model be applied to
    conservation and protected areas? What are its limits?

-   Can the impacts of land use change be evaluated as a portfolio
    problem?

## Progress Report

This project was funded to commence January 1, 2001. Since that time, a
Memorandum of Understanding managing the cooperation between the USGS
and NASA was written and signed; a similar Memorandum of Agreement
between the USGS and the private research partners was also signed.

The first of the concession areas, the Parque Nacional Cordillera Azul
in Perú, was created in 2001. This area was established in advance of
our research, so we will be able to use the data collected there to
build models and tools for future protected areas. The Field Museum and
CI have continued to collect remote sensing and ground-tested data in
Bolivia, Perú, and other potential future concession sites. The Field
Museum has completed and published two Rapid Biological Inventories
(Biabo, Perú, and Pando, Bolivia).

The USGS and HGA have agreed to investigate the suitability of the
portfolio model to forest land-use and investment in protected areas.
Data from several study areas---including various types of remote
sensing, GIS layers, and socioeconomic indicators---are being collected
and will be delivered to the CSP in batches.

An initial literature search has been conducted for the project; the
resulting bibliography is being augmented continually. The next section
outlines the economic models and ideas we will be using; the following
section describes the literature review and annotated bibliography.

# The Economic Model for a Land Use Portfolio

Define *V~k~* to be the market or nonmarket value of land parcel in
location *k*, where *k*=1,...,*K* locations in a tropical ecosystem.
Local physical conditions can affect *V~k~*. Value is a function of the
willingness to pay for an economic activity to occupy a parcel of land
and represents land rent and any surplus that accrues in the forms of
either consumer's or producer's surplus (Lind, 1973). Value can be based
on market prices (private goods) and nonmarket benefits (environmental
goods and services). Development pressures can cause environmental
hazards (adverse physical changes due to human intervention and/or
natural hazard) that can reduce *V~k~*. Support for estimation of a
change in land value is an environmental hazard probability that is a
function of natural science process models and information that vary in
accuracy and density of observations from region to region (Bernknopf
and others, 2001). Where an environmental hazard is present for a land
parcel, the total expected benefit becomes

> ![](media/image2.wmf) (1)

where *p~haz~* = the probability of the hazardous event occurring.

The effectiveness of any specific remedy *~eff~*, including
concessions, is uncertain for a specific site, where
$0 \leq \psi_{\text{eff}} \leq 1$. There is no certainty that
regulations can be enforced completely even though property rights have
been defined completely. Lack of enforcement creates a risk to the
regulatory authority. Combining hazard probability and concession yields
a vulnerability assessment, **, where ** = 1, . . . , **. Although
their outcomes are uncertain, concession actions are taken to reduce the
impact of the hazard on *V~k~*. However, the decision to mitigate a
hazard comes at cost *C~fc~*. When a concession is undertaken, equation
1 becomes

![](media/image3.wmf) (2)

where $0 < \gamma \leq 1$, and *C~cf~*~(*k*)~ is the cost of a
concession in *k*.

The land use portfolio contains safe and risky assets. The portfolio can
be described in terms of a proportion *~h~* of *j* activities that are
*h* = 0 for vacant land (that is, a riskless asset), *h* = 1 for forest
land developed with a safe land use, *h* = 2 for land with activities
susceptible to specific hazards, and *h* = 3 for land with activities
that are subject to specific hazards that have been mitigated. Land
parcels subject to a hazard could suffer damage from development, and
the use and future value of these lands would diminish. In these
parcels, there is a risk associated with the benefits from the land use
owing to the severity of hazard. In addition, benefits of concessions
are uncertain because the effectiveness of the remedy is ambiguous; that
is, when a hazard event occurs there could be a total loss or partial
damage. The value of a forest land use portfolio A is therefore

> $V(A) = \sum_{k}^{}{\alpha 0} + \sum_{k}^{}{\alpha h}V\text{jk}$ *h* =
> 1,2; *j* = 1,...,*J* (3)

where *V*(*A*) is the value of the land use portfolio *A*, *j* is the
potential land use, *j* =0,...,*J*, and *α~h~* is the proportion of the
portfolio in hazard class *h*. In locations that incur a concession
investment, the value of portfolio *A* now becomes

> $V^{'}(A) = \sum_{k}^{}{\alpha 0} + \sum_{k}^{}{\alpha hV\text{jk} +}\sum_{k}^{}{\alpha{hV}^{'}\text{jk}}$
> *h* = 1,2,3; *j* = 1,...,*J* (4)

where $0 < V^{'}(A\text{JK}) \leq V(A\text{JK})$.

## Economic Decisionmaking

It is assumed that the decisionmaking preference is to maximize the
value of a forest land use portfolio and, where appropriate, to lessen
development and environmental risk. The economic decisionmaker evaluates
a forest portfolio with development risks defined in terms of objective
probabilities and assumes rational behavior under risk. Information
about the hazard and the effectiveness of loss avoidance contains
uncertainty.

In the decision problem, it is assumed that an economic action is to
invest in a forest concession with or without loss prevention from
development or natural hazards. This economic action is represented by
an objective probability distribution of a property valuation *V*(*A*)
(Sinn, 1983):

> $R\lbrack V(A)\rbrack = \sum_{j = 0}^{J}{\sum_{k = 1}^{K}{pkV}\text{jk}}$
> (5)

where $R( \cdot )$is a function of preferences, *p~k~* which are the
probability that portfolio *A* will have value *V* associated with
wealth *W~K~*. In other words, the decisionmaker chooses an action out
of the set of possible alternatives that brings about a probability
distribution that maximizes the value of the preferences (Sinn, 1983).
It is assumed that *V*(*A*) is a random variable that takes on a range
of values with known probabilities *p~k~*. So far we have developed this
using a quadratic utility function. We believe that a state preference
approach to the forest land use portfolio is better suited for this
project. Our research has begun to develop this model. Until the state
preference approach is fully developed, we are substituting the
quadratic utility model. Consider a quadratic function that relates
utility *u* to *V* in the following manner (Sharpe, 1970):

> $u\text{jk} = a + \text{bV}\text{jk} - \text{cV}\text{jk}^{2}$ (6)

A summary of individual preferences is a utility function, and the
decision criterion for these preferences is the expected utility
criterion. The expected utility criterion is defined as the mathematical
expectation of an end-of-period wealth distribution that is transformed
into a distribution of utilities by means of a given utility function
(Sinn, 1983). Define *EU*(*A*) as the expected utility of portfolio *A*:

$\text{EU}(A) = \sum_{j = 0}^{J}{\sum_{k = 1}^{K}{pku\text{jk}} =}\sum_{j = 0}^{J}{\sum_{k = 1}^{K}{pk(a + \text{bV}\text{jk} - \text{cV}\text{jk}^{2}}})$
(7)

Expanding equation 7 yields

$\text{EU}(A) = a\sum_{k = 1}^{K}{pk} + b\sum_{j = 0}^{J}{\sum_{k = 1}^{K}{pkV\text{jk}} - c\sum_{j = 0}^{J}{\sum_{k = 1}^{K}{pkV\text{jk}^{2}}}}$
(8)

where
$\sum_{k = 1}^{K}{pk = 1},\sum_{j = 0}^{J}{\sum_{k = 1}^{K}{pkV\text{jk} = E(A),\text{ and }\sum_{j = 0}^{J}{\sum_{k = 1}^{K}{pkV\text{jk}^{2} = \sigma^{2}(A) +}}}}E^{2}(A)$

The expression for expected utility is

$\text{EU}(A) = a + \text{bE}(A) - \text{cE}^{2}(A) - {c\sigma}^{2}(A)$
(9)

where *EU'* \> 0, and *EU" ≤ 0* (Lewis and Nickerson, 1989; Shogren and
Crocker, 1991; and Quiggen, 1992).

# Literature Review

The relevant literature for this project can be divided into four main
categories:

-   Remote sensing and GIS capacities, limitations, and applicability to
    protected areas

-   Biodiversity classification, measurement, and prioritization

-   Economic optimization, portfolio theory, spatial analysis, risk
    management, and decision analysis

-   Protected areas creation and management

Obviously, these are broad topics with several journals devoted to each,
and an exhaustive study of any one of them would take years. Here, we
outline the state of research in each of the above categories and define
the important research concepts from each topic, the state of the
research, and how we will add to them. For this project, we focus on
those aspects of the literature that are most critical---either in terms
of technique, concept, or geographic relevance to the case studies.

## Remote Sensing and GIS

The first stage of the project involves making decisions about which
types and scales of remote sensing data to acquire. Thus, most efforts
to date have been dedicated to this category. One of the major
opportunities for decreasing the risk and increasing the
cost-effectiveness of conservation is to optimize the amount, type, and
scale of data used. Field observations and remote sensing are the two
ways of gathering information about the condition of and threats to
tropical forests ecosystems. Both provide information to help make
decisions, and both bear a cost. The goal is to maximize the utility of
the information acquired per dollar spent on it.

To reach this goal, we will research the many types of remote sensing
data available. These might be from government (such as Landsat thematic
mapper) or private (like Space Imaging Corporation's IKONOS) sources. We
seek to determine the appropriateness of each technology on the basis of
relative resolution, costs and scales of data, and the sensitivity to
the physical structures that we will need to image. There are likely to
be other constraints as well; for example, some technologies are more
capable of penetrating the cloud cover that is so prevalent in the
tropics.

Since data costs increase with scale and resolution, we are looking for
"thresholds" in the relations between these variables. We wish to know
at what scales information becomes useful and at what point buying
higher resolution information brings diminishing returns. A similar
dynamic exists in the search for the ideal number of different types and
combinations of data to supply. This aspect of the project is very
similar to a number of USGS--CSP projects that combine economic
analysis, optimization, risk management, and spatial modeling in GIS
frameworks (see section on Economic Optimization, Portfolio Theory,
Spatial Analysis, and Risk Management, below).

At present, one major application of remote sensing is ecosystem and/or
vegetation classification. Since the overarching goal is to protect
biodiversity, having an accurate and efficient way to categorize forests
(and thus ecosystems) is fundamental. Fortunately, the literature on
this is extensive. The most promising references for this section are
Brondizio and others (1996), Coulter and others (2000), Jorge and
Blanco-Garcia (1997), Lawrence and others (1995), Lyon and others
(1998), although others in the annotated bibliography below are useful
as well. Our applications of the remote sensing and GIS technologies to
ecosystem classification are not particularly novel in and of
themselves. They are unique, however, in combination with the economic
analyses and risk management to which we will link them. The literature
on those steps is discussed next.

## Economic Optimization, Portfolio Theory, Spatial Analysis, and Risk Management

This part of the project involves the tools, techniques, and principles
that form the underpinnings of the analyses we will use. Again, each of
these disciplines has a substantial literature associated with it, and
we are adapting that literature to our purposes. The "Economic Model for
a Land Use Portfolio" section above details many of the most important
concepts and references some of the pertinent literature, but other
concepts are discussed here.

One primary method of solving a problem of constrained optimization is
through the use of linear programming (LP). The basics of LP are
discussed in Murty (1983), Bazarra (1990), and many other texts.
Research linking biological or ecological inputs with economics ones in
an LP is common (see, for example, Paulsen and Wernstedt, 1995, and
Taylor and others, 1992). However, this project would extend this
application because of the novel objective function and constraints that
grow out of the conservation concession approach. Further, the risk
management perspectives and portfolio model of land use applied to
private investment in conservation seems to be a rare combination.

Combining all of these within spatial analytical frameworks and a GIS
involves further extensions of existing work, much of it done by the
workers now with the USGS--CSP (see Bernknopf and others, 1997;
Bernknopf and others, 1998; Pike and others, 1994). As noted above,
spatial and statistical analysis of physical processes and their
socioeconomic impacts is becoming more common. Griffith (1999),
Fotheringham (2000), Ripley (1988), Cressie (1991), Pacione (1999), and
others have completed texts, casebooks, and surveys of techniques that
provide theoretical foundations for this project. Finally, this project
will also use the more traditional analytical frameworks such as
cost-benefit analysis (see Boardman and others, 1996), dynamic
optimization (Chiang, 1992), Bayesian statistics (Lee, 1989), and
decision analysis under economic uncertainty (McKenna, 1986).

## Biodiversity Classification and Prioritization

The purpose of this part of the project is to build upon the
cost-effective information about forest structure and the spatial,
economic, and statistical analysis methods from the above section. We
will investigate the tools that our research partners are already using
to measure biodiversity and prioritize certain ecosystems and parcels of
forest for protection. Currently, there are indices based on biological
or ecological metrics that assess how much biodiversity is present in an
area. These indices might focus on species richness, degrees of rarity
or endemism, genetic diversity, or numbers of higher order taxa
contained in the area. The purpose of the USGS--CSP is not to rework
these classification and prioritization systems---this is the domain of
the research partners. Rather, we seek to understand the objectives that
are being used, to formalize and operationalize their measurement, and
to incorporate them into the analytical tools and risk management models
we create.

The researchers from the Field Museum of Natural History have completed
extensive biological inventories and ecological assessments on areas
that CI has considered for inclusion into concession areas (see, for
example, Alverson and others, 2000). In the study areas themselves,
there are frequently government or private research institutions that
study the ecology and structure of ecosystems as part of their internal
planning and/or zoning processes. One example is the Iwokrama Center for
Rainforest Conservation and Development (2000), which zoned different
parts of the forests for timber, wilderness, tourism, villages, and so
on. Wherever possible, we will use these types of analyses. Finally,
there are general studies on biodiversity measurement and prioritization
methods. Gaston (1996), Lawton and others (1998), and Spellerberg (1992)
all investigate and/or develop systems for ranking areas in terms of
their biodiversity and ecological importance. Smith (2000) compiled a
large text that lists by category all the biodiversity research
published in peer-reviewed literature.

Again, USGS--CSP's goal for this project is not to "improve" the
measurement or prioritization methods used in protected areas creation
and management, but to incorporate them into the spatial, risk, and
economic optimization models that we are developing. By making these
methods a part of the GIS, especially, we can provide a set of tools
that can be used in the Bayesian management structure discussed above.
This will allow for rapid updating of protected area status and
ecosystem condition and facilitate better management decisions.
Significantly, this integration of ecological priorities into GIS, risk,
and economic models will be available to other conservation agencies
that could use this method to formalize their own decision criteria.

## Protected Areas and the Economics of Conservation

There is a large body of literature on protected areas creation and
management in general, and on the need for and difficulty of attaining
economic efficiency in conservation in particular. Examples of general
work abound. Witting and Loesscheke (1993) explore whether the goal
should be reserve optimization or minimization of species loss. Polasky
(1998) investigates the problems in resolving private property rights
with biodiversity conservation. Main and others (1999) assess the costs
of conservation. Dreschler and Watzold (2001) model economic efficiency
in allocation of biodiversity conservation funds between two areas.
Kremen and others (2000) explored how effective different economic
incentives at various scales were in achieving conservation. Ando and
others (1998) demonstrate how cost-effectiveness of endangered species
protection can be maximized across the United States by prioritizing
habitat protection on the basis of land price. The historical and
regional trends in biodiversity investment were investigated by
Abromovitz (1991).

Also common is research directed to a method of protection, much of it
conducted by our research partners. Gullison and Rice (2000) explored
the prospect of marketing species conservation; Hardner and Rice (1999)
analyzed forest concession policies to see how they might be used to
increase and improve conservation. Bruner and others (2001) determined
that parks were largely successful at protecting biodiversity,
especially if adequate funding for policing was provided. Wong and
Gullison (2000) surveyed catastrophic losses in protected areas and
developed a system for risk assessment and management. Jackson and
Gullison (2001) surveyed the budget size and composition of protected
areas in various countries.

There are, of course, numerous other studies of protected areas in the
regions in which our case studies will take place. However, our research
has not yet progressed to that point. We have targeted the early work
toward the remote sensing and data needs and gaining an understanding of
the methods, needs, and structures of our research partners. The final
section of this report is an annotated bibliography, which contains full
reference for all of the works cited above. Other texts and papers are
listed that are relevant, but were not specifically mentioned above.

# Annotated Bibliography

## Remote Sensing and Geographic Information Systems

Brondizio, Eduardo; Moran, Emilio; and Mausel, Paul, 1996, Land cover in
the Amazon estuary: linking of the thematic mapper with botanical and
historical data: Photogrammetric Engineering and Remote Sensing, v. 62,
p. 921-929.

-   The paper investigates whether 30-meter Landsat TM data might be
    able to do what the Landsat multispectral scanner (MSS) and the
    advanced very high resolution radiometer (AVHRR) could not: track
    post-deforestation processes and map land cover and land use
    classifications. The results indicate that the higher spatial and
    spectral resolutions make this a workable option that can achieve
    81-100 percent accuracy. Fourteen classes were delineated, but
    global positioning system (GPS) test and training data were needed,
    and field observations were linked as well. The approach was
    adequate for identifying forests that were economically significant,
    and the results are clear enough to use in global carbon cycle
    modeling. The study site was a low-lying Brazilian floodplain
    forest.

Coulter, Lloyd; Stow, Douglas; and Hope, Allen, 2000, Comparison of high
spatial resolution imagery for efficient generation of GIS vegetation
layers: Photogrammetric Engineering and Remote Sensing, v. 66, no. 11,
p. 1329-1335.

-   The authors found that high-resolution digital orthophotoquads can
    be used to make GIS layers of the Southern California desert for
    vegetation classification with greater than 75 percent accuracy.
    They found that ground-tested information was about 10 percent
    better than that, but much more expensive. Key questions are whether
    or not this approach would work in the tropics, and to what degree
    the classification went. It may not be fine enough for our purposes.

Edwards, G., and Lowell, K.E., 1996, Modeling uncertainty in
photointerpreted boundaries: Photogrammetric Engineering and Remote
Sensing, v. 62, p. 377-391.

-   This paper details a model for local boundary uncertainty estimation
    (fuzzy boundaries) that is fine enough to distinguish between
    separate stands of a forest. It includes two aspects of perception:
    discrimination and variability.

Eiumnoh, Apisit, and Shrestha, R.P., 2000, Improving vegetation
classification in wet--dry forest ecosystems using DEM and Landsat TM:
Photogrammetric Engineering and Remote Sensing, v. 66, no. 3, p.
318-327.

-   The authors applied DEM and other ancillary remote sensing to
    Landsat TM images of wet-dry forests in Thailand to improve
    classification into land use and land cover maps. The results showed
    that classification accuracy can be improved from 65 percent up to
    75 percent through the use of the added data. If the classification
    is supervised, this improves further to 84 percent.

Foody, Giles M., 1994, Ordinal-level classification of subpixel tropical
forest cover: Photogrammetric Engineering and Remote Sensing, v. 60, p.
61-65.

-   The conclusion of this paper was that it might be possible to
    predict subpixel ordinal classification of tropical forest cover
    with 75 percent accuracy, when using a four-group classification
    system. Using data from NOAA's AVHRR satellite data, with a typical
    spatial resolution of 1.1 km^2^ pixels, the author developed a
    system that could fairly accurately sort areas smaller than that
    pixel size into categories of forest. Since AVHRR is the lowest
    resolution (and thus the lowest cost) remote sensing technology,
    being able to use it to target areas for further study with costlier
    methods could improve cost-effectiveness significantly.

Fuller, R.M., Groom, G.B., Mugisha, S., Ipulet, P., Pomeroy, D.,
Katende, A., Bailey, R., and Ogutu-Ohwayo, R., 1998, The integration of
field survey and remote sensing for biodiversity assessment: a case
study in the tropical forests and wetlands of Sango Bay, Bolivia:
Biological Conservation, v. 86, p. 379-391.

Jorge, Luis Alberto and Blanco-Garcia, Gilberto Jose, 1997, A study of
habitat fragmentation in southeastern Brazil using remote sensing and
GIS: Forest Ecology and Management, v. 98, p. 35-47.

-   This paper used remote sensing and GIS frameworks to evaluate the
    effects of habitat fragmentation. It measured edge effects, canopy
    changes, and other ecological effects in an attempt to demonstrate
    that these technologies can adequately track such changes. This is
    an approach that we could build on.

Keech, Maurice A., 1990, Remote sensing applied to ecological
degradation: Chemistry and Industry, v. 9, p. 296-298.

Kimura, Hiroshi, and Yamaguchi, Yasushi, 2000, Detection of landslide
areas using satellite radar inferometry: Photogrammetric Engineering and
Remote Sensing, v. 66, p. 328-336.

-   SAR was used to monitor the behavior of landslides that were
    otherwise missed by GPS. One of the goals of our project is to use
    the remote sensing data that are collected for verification to
    monitor natural changes in or around the protected areas. This study
    shows that at least one type and scale of remote sensing is
    appropriate for such a use. We will need to investigate others, and
    also investigate other natural changes.

Lambin, Eric F., 1996, Change detection at multiple temporal scales:
Photogrammetric Engineering and Remote Sensing, v. 62, p. 931-938.

-   The main point of this paper was that attempts using remote sensing
    to detect long-term changes over time in tropical forests that are
    remotely sensed often fail. This is because seasonal variation is
    much greater than annual or long-term changes, so that long-term
    changes are lost amid the noise of seasonal cycles. The authors use
    three West African landscapes to explore the effect of more frequent
    sampling and data aggregation. The idea is that aggregating data
    from many collections will smooth out the noise and give a truer
    picture of the long-term changes, if any. The paper is more centered
    on learning how to aggregate the data than on determining how often
    to resample. This is an important point to be aware of as we start
    looking for optimal remote sensing expenditure points.

Lawrence, William; Saatchi, Sasan; DeFries, Ruth; Dietz, James; Rice,
Richard; Dietz, Lou Ann; Siguiera de Araujo, M.; and Alger, Keith, 1995,
Utilization of SAR and optimal remote sensing data for habitat
conservation in the tropical forest of Brazil: Proceedings of the
International Geoscience Remote Sensing Symposium, v. 2, p. 1480-1482.

Lyon, J.G.; Yuan, Ding; and Lunetta, Ross S., 1998, A change detection
experiment using vegetation indices: Photogrammetric Engineering and
Remote Sensing, v. 64, p. 143-150.

-   A test of seven different vegetation indices based on data from the
    Landsat multispectral sensor showed that the Normalized Green
    Vegetation Index was best at vegetation classification. The site was
    in Chiapas, Mexico. There was a history of significant disturbance
    in the area---fires, deforestation, reservoir construction, and some
    postclearing regrowth. At the very least, this paper gives a good
    explanation of seven vegetation indices that we should consider
    using for this project.

Myers, Norman, 1988, Tropical deforestation and remote sensing: Forest
Ecology and Management, v. 23, p. 215-225.

-   This older article is a good introduction to the fundamentals of
    remote sensing technology. It is especially good because it
    demonstrates early ideas about how remote sensing could be used to
    investigate tropical deforestation and ecosystem disruption. It is a
    good contrast to today's more highly technical papers and shows how
    far the technology has come in just 13 years.

Nagendra, Harini and Gadgil, Madhav, 1999, Biodiversity assessment at
multiple scales: linking remotely sensed data with field information:
Proceedings of the National Academy of Sciences of the USA, v. 96, no.
16, p. 9154-9158.

Riou, Robert and Seyler, Frederique, 1997, Texture analysis of tropical
rain forest infrared satellite images: Photogrammetric Engineering and
Remote Sensing, v. 63, p. 515-521.

Roy, P.S. and Tomar, Sanjay, 2000, Biodiversity characterization at
landscape level using geospatial modeling technique: Biological
Conservation, v. 95, no. 1, p. 95-109.

-   The authors develop a method of characterizing GIS grid cells in an
    ecosystem in India. Patchiness, interspersion, juxtaposition,
    biological richness, digital elevation models, terrain complexity,
    disturbance index, and biodiversity value all come together to form
    the system. It could be useful to compare this with CI's system and
    others to see which might be best, easiest, and cheapest to use in
    our study areas.

Savitsky, Basil G., and Lacher, Thomas E., Jr., eds., 1998, GIS
Methodologies for Developing Conservation Strategies: New York, N.Y.,
Columbia University Press.

-   This book has a brief overview and three main sections. The first
    section is an introduction to the logic and terminology of digital
    mapping technologies, including GIS's, satellite image analysis, and
    global positioning systems. The second is a five-chapter set of
    examples of GIS applied to conservation in Costa Rica. The third is
    a six-chapter case study of Gap Analysis as used in a USAID project.
    The early parts of the book are useful in outlining the basics of
    remote sensing and GIS, and the case studies are useful and lucid
    illustrations of how the technology is used to build models, plan
    and prioritize conservation strategies, and assist in
    decisionmaking.

Skole, D., Chomentowski, W.H., and Salas, W.A., 1994, Physical and human
dimensions of deforestation in Amazonia: BioScience, v. 44, p. 314-322.

Sohn, Y., Moran, E., and Gurri, F., 1999, Deforestation in north-central
Yucatan: Mapping secondary succession of forest and agricultural land
use in Sotuta using the cosine of the angle concept: Photogrammetric
Engineering and Remote Sensing, v. 65, no. 8, p. 947-958.

Stone, Thomas; Schlesinger, Peter; and Houghton, Richard, 1994, A map of
the vegetation of South America based on satellite imagery:
Photogrammetric Engineering and Remote Sensing, v. 60, p. 541-551.

-   The authors developed a digital vegetation map of the entire
    continent of South America at 1-km resolution. We obviously need
    finer resolution, but it's a good start on continental-scale
    vegetation delineation.

Stoms, David, 1992, Effects of habitat map generalization:
Photogrammetric Engineering and Remote Sensing, v. 58, p. 1587-1591.

-   This paper investigates how the scale of GIS information affects
    decisions about biodiversity measurement and prioritization. A case
    study in the southern Sierra Nevada in California measured the
    extent to which decreasing loss of scale "blurred out" smaller
    habitat parcels and altered the ensuing measurements of species
    richness. This is an extremely relevant concept for our project,
    because part of what we will try to do is optimize the amount and
    scale of remote sensing and GIS information that conservation groups
    need to purchase. We also need to investigate how tropical forests
    differ from temperate mountain forests.

Todd, Stella, and Hoffer, Roger, 1998, Responses of spectral indices to
variations in vegetation cover and soil background: Photogrammetric
Engineering and Remote Sensing, v. 64, no. 9, p. 915-921.

-   This paper evaluates vegetation cover and the soil background's
    effects on the green vegetation index (GVI) and on the normalized
    green vegetation index (NGVI), as imaged by the Landsat TM. The
    results showed that GVI was more accurate than NGVI and less
    dependent on existing knowledge about soil background moisture. It
    may or may not be useful to our work to use one of these indices
    instead of developing our own.

Trietz, Paul, and Howarth, Philip, 2000, Integrating spectral, spatial,
and terrain variables for forest ecosystem classification:
Photogrammetric Engineering and Remote Sensing, v. 66, no. 3, p.
305-317.

-   The authors used information from the compact airborne
    spectrographic imager (CASI) to classify boreal forests with low to
    moderate relief. By using spectral, spectral-spatial, and terrain
    variables, they significantly improved the classification, showing
    that large gains are possible by effectively bundling information in
    this way.

Verbyla, D.L. and Richardson, C.A., 1996, Remote sensing clearcut areas
within a forested watershed: comparing SPOT HRV panchromatic, SPOT HRV
multispectral and Landsat thematic mapper data: Journal of Soil and
Water Conservation, v. 51, no. 5, p. 423-427.

-   This article explores the ability of three remote sensing types
    (SPOT HRV panchromatic, SPOT HRV multispectral, and Landsat TM) to
    detect clearcut areas by identifying exposed soil and distinguishing
    it from nonexposed soil. The three types were very close to each
    other, with only the SPOT panchromatic being slightly less accurate.
    All were comparable with aerial photographs at 1:15,840 scale. The
    mean accuracy was 85 percent; omission error rates were \< 5
    percent, and commission errors were between 2 and 37 percent. Since
    conservation concessions require verification that forests have been
    maintained before payments are made, CI will likely use something
    similar to this paper's method. This presents an opportunity to use
    those remote sensing images to detect other changes in and around
    the forest for use in managing the protected areas.

## Economic Optimization, Portfolio Theory, Spatial Analysis, and Risk Management

Bazarra, M.S.; Jarvis, J.J.; Sherali, H.D., 1990, Linear programming and
network flows: New York, NY, John Wiley and Sons, Inc.

Bernknopf, R.L., and Karl, Herman, 1998, Thoughts on the application of
science to decisionmaking under uncertainty: the nexus between science
and policy: Water Resources Update, v. 114, p. 14-20.

Bernknopf, R.L.; Brookshire, D.S.; McKee, M.; and Soller, D.R., 1997,
Estimating the social value of geologic map information: a regulatory
application: Journal of Environmental Economics and Management, v. 32,
p. 204-218.

Bernknopf, R.L.; Campbell, R.H.; Brookshire, D.S.; and Shapiro, C.D.,
1998, A probabilistic approach to landslide hazard mapping in
Cincinnati, Ohio, with applications for economic evaluation: Bulletin of
the Association of Engineering Geologists, v. 25, p. 39-56.

Boardman, A.E.; Greenberg, D.H.; Vining, A.R.; and Wiemer, D.L., 1996,
Cost-benefit analysis---concepts and practice: Upper Saddle River, NJ,
Prentice-Hall, Inc.

Chiang, Alpha C., 1992, Elements of Dynamic Optimization: New York, NY,
McGraw-Hill, Inc.

Cressie, Noel C., 1991, Statistics for Spatial Analysis: New York, NY,
J. Wiley Inc.

Dixon, J.B. and Sherman, P.B., 1990, Economics of protected areas: a new
look at benefits and costs: Washington, D.C., Island Press.

Fotheringham, A.S., 2000, Quantitative geography: perspectives on
spatial data analysis: London, SAGE Publications.

Fotheringham, A.S., and Wegener, Michael, eds., 2000, Spatial models and
GIS: new potential and new models: London, Taylor and Francis
Publications.

Griffith, Daniel A., 1999, A casebook for spatial statistical analysis:
a compilation of analyses of different thematic data sets, London and
New York, Oxford University Press.

Kunreuther, H.; Platt, R.; Bernknopf, R.L.; Buckley, M.; Burkett, V.;
Conrad, D.; Davison, T.; Deutsch, K.; Geis, D.; Good, J.; Jannereth M.;
Knap, A.; Lane, H.; Ljung, G.; Mcauley, M.; Mileti, D.; Miller, T.;
Morrow, B.; Myers, J.; Pielke, R.; Pratt, A.; and Tripp, J., 1999, The
hidden cost of coastal hazards: implications for risk assessment and
mitigation: The John Heinz Center for Science, Economics and
Environmental Policy, Island Press.

Lee, Peter M., 1989, Bayesian statistics: an introduction: New York, NY,
Oxford University Press.

Lewis, Tracy, and Nickerson, David, 1989, Self-insurance against natural
disasters: Journal of Environmental Economics and Management, v. 16, p.
202-223.

Lind, R.C., 1973, Spatial equilibrium, the theory of rents, and the
measurement of benefits from public programs: The Quarterly Journal of
Economics, v. 87, p.188-207.

McKenna, C.J., 1986, The Economics of Uncertainty: Sussex, U.K.,
Harvester Press Publishing Group.

This slim textbook contains a useful primer on decisionmaking under
uncertainty, expected utility theory, consumption and savings under
uncertainty, and an introduction to portfolio theory. It is not heavily
mathematical, yet is a valuable introduction and/or review of
fundamentals of risk-based economic analysis.

Murty, Katta G., 1983, Linear programming: New York, NY, John Wiley and
Sons, Inc.

Pacione, Michael, 1999, Applied geography principles and practice: an
introduction to useful research in physical, environmental and human
geography: London, Routeledge Press.

Paulsen, Charles M., and Wernstedt, Kris, 1995, Cost-effectiveness
analysis for complex managed hydrosystems: an application to the
Columbia River basin: Journal of Environmental Economics and Management,
v. 28, p. 388-400.

-   The authors link a salmon population simulation model with a linear
    program-based optimization model to determine a cost-effective set
    of hydrosystem management actions that would allow recovery of
    salmon stocks in the Columbia River.

Pike, R.J.; Bernknopf, R.L.; Tinsley, J.C. III; and Mark, R.K., 1994,
Hazard of earthquake-induced lateral-spread ground failure on the
central California coast modeled for earth science map data in a
geographic information system: U.S. Geological Survey Open-File Report
94-662.

Proceedings from the Second International Symposium on Spatial Accuracy
Assessment in Natural Resources and Environmental Sciences, 2000,
Quantifying spatial uncertainty in natural resources: theory and
applications for GIS and remote sensing: Chelsea, Mich., Ann Arbor
Press.

Quiggen, John, 1992, Risk, self-protection and ex ante economic value:
some positive results: Journal of Environmental Economics and
Management, v. 23, p. 40-53.

Ripley, B.D., 1988, Statistical inference for spatial processes:
Cambridge, U.K., Cambridge University Press.

Shogren, J.F. and Crocker, T.D., 1991, Risk, self-protection and ex ante
economic value: Journal of Environmental Economics and Management, v.
20, p.1-15.

Sinn, Hans-Werner, 1983, Economic decisions under uncertainty:
Amsterdam, North-Holland Press.

Taylor, M.L.; Adams, R.M.; and Miller, S.F., 1992, Farm-level response
to agricultural effluent control strategies: the case of the Willamette
Valley: Journal of Agricultural and Resource Economics, v. 17, no. 1, p.
173-185.

This paper links an agricultural production simulation model with a
linear program to predict farm-level responses to various policies
designed to control runoff. The Erosion-Productivity Impact Calculator
(EPIC) was used to simulate the agricultural production, erosion, and
effluent that could result from various responses to the policies above.
A linear program was then run using the General Algebraic Modeling
System (GAMS) to simulate the set of practices that would be taken by
farmers seeking to maximize profit under the constraints of the various
policies.

## Biodiversity Classification and Prioritization 

Alverson, W.S., Moskovits, D.K., and Shopland, J.M., eds., 2000,
Bolivia-Pando, Rio Tahuamanu---Rapid biological inventories report 1:
Chicago, Ill., The Field Museum.

Blockhus, J.M., Dillenbeck, M.R., Sayer, J.A., and Wegge, Per, eds.,
1992, Conserving biological diversity in managed tropical forests:
Gland, Switzerland and Cambridge, U.K., The International Union for
Conservation of Nature and Natural Resources--World Conservation Fund.

-   The 1990 meetings of the International Tropical Timber Organization
    (ITTO) resulted in a resolution that all tropical timber should be
    harvested from sustainably managed forests by 2000. This text
    examines the legal and practical obstacles and methods that such a
    project entails. Though our project does not attempt to manage a
    sustainable timber harvest---in fact, it appears as an alternative
    solution to the failures of such efforts---it might be useful to
    investigate where and how sustainable forestry fails to that those
    pitfalls can be avoided. This document also contains much
    information that will be useful in our case studies. A chapter for
    each country outlines its biodiversity, the extent and security of
    productive forests, classifications of soil and forest types, and
    logging practices and rates.

Gaston, K.J., ed., 1996, Biodiversity: a biology of numbers and
difference, Oxford, U.K., Blackwell Press.

Iwokrama Center for Rainforest Conservation and Development, 2000,
Zoning of the Iwokrama forest---working paper: Georgetown, Guyana.

-   The Iwokrama Forest in Central Guyana is located near the study area
    where a conservation concession is being established. This paper
    documents the division of the forest into a sustainable utilization
    area and a designated conservation area. It tracks the biophysical
    and ecological variables that are relevant in Iwokrama and likely to
    be factors in the concession area as well. It also analyzes the
    socioeconomic issues and identifies stakeholders that are critical
    to successful protected areas. Together, the patterns seen in
    physical and social data will be most useful in the first case study
    our project will analyze. We are currently corresponding with the
    authors to determine what GIS and remote sensing information they
    had, and what additional information they would have benefited from
    having.

Lawton, J.H.; Bignell, D.E.; Bolton, B.; Bloemers, G.F.; Egglteon, P.;
Hammond, P.M.; Hodda, M.; Holt, R.D.; Larsen, T.B.; Mawdsley, N.A.;
Stork, N.E.; Srivastava, D.S.; Watt, A.D., 1998, Biodiversity
inventories, indicator taxa, and effects of habitat modification in
tropical forest: Nature, v. 391, p. 72-76.

Ramdass, I., and Hoosein, M., 1999, Vital in-situ and ex-situ sites for
flora and fauna management in Guyana: Georgetown, Guyana.

Smith, Charles M., 2000, Biodiversity studies: a bibliographic review:
Lanham, M.D., and London, Scarecrow Press, Inc.

Spellerberg, Ian F., 1992, Evaluation and assessment for conservation:
London, U.K., Chapman and Hall.

-   This book describes a thorough system for measuring, assessing,
    valuing, prioritizing, and protecting biodiversity and ecosystems.
    Again, our research partners have their own methods for doing many
    of those things, and the purpose of this project is not to replace
    those systems, but optimizing any system requires knowing what the
    alternatives are. This book presents an extremely detailed approach
    to all ecological facets of biodiversity conservation and is useful
    for background knowledge on the issues.

Zagt, R., and ter Steege, H., 1999, Tree diversity in Guyana: critical
forest regions for the establishment of a protected areas system:
Tropenbos-Guyana Programme/ Utrecht University.

## Protected Areas and the Economics of Conservation

Abromovitz, J.N., 1991, Investing in biological diversity: U.S. research
and conservation efforts in developing countries: New York, NY, World
Resources Institute.

-   This book reports the results of a 1990 survey that sought to
    measure the amount, source, and types of investments in
    international biodiversity and conservation. It identifies major
    patterns in private and U.S. Government investment in various
    regions of the globe. It also breaks down how much of an average
    conservation dollar went to research, site management, policy
    planning, education, and so on. These patterns are now 10 years old,
    but they still provide a useful metric for assessing the
    cost-effectiveness of today's conservation investments and
    strategies.

Ando, Amy; Camm, Jeffrey; Polasky, Stephen; and Solow, Andrew, 1998,
Species Distributions, Land values, and efficient conservation: Science,
v. 279, p. 2126-2128.

-   The authors use county-level data on land prices and incidence of
    endangered species to assess the cost effectiveness of protecting
    endangered species. By considering heterogeneity in land prices and
    the concomitant changes in the cost of setting aside land for
    endangered species habitat, they show that substantial efficiency
    gains can be made either in the cost of covering a certain number of
    species, or in maximizing the coverage allowed by a fixed budget.

Attwell, C.A.M., and Cotterill, F.P.D., 2000, Postmodernism and African
conservation science: Biodiversity and Conservation, v. 9, p. 559-577.

Bonnie, Robert; Schwartzmann, Stephan; Oppenhiemer, Michael; and
Bloomfield, Janine, 2000, Counting the cost of deforestation: Science,
v. 288, p. 1763-1764.

Bruner, A.G.; Gullison, R.E.; Rice, R.E.; and de Fonseca, G.A.B., 2001,
Effectiveness of parks in protecting tropical forest biodiversity:
Science, v. 291, p. 125-128.

-   The authors investigated threats to protected areas from human
    impacts in 93 protected areas in 22 countries. They found that parks
    are largely successful in preventing land clearing, fire,
    unauthorized hunting, and grazing. Basic park management activities,
    especially when combined with compensation to local communities,
    contribute to large increases in effectiveness.

Dreschler, Martin, and Watzold, Frank, 2001, The importance of economic
costs in the development of guidelines for spatial conservation
management: Biological Conservation, v. 97, p. 51-59.

-   This paper investigates biodiversity-enhancing land use to see which
    spatial allocations of subsidies for this purpose might increase
    efficiency. The authors build a two-area economic model to determine
    the optimal allocation of resources between the areas and show that,
    as marginal costs of conservation increase, allocations approaching
    equal between the two areas become more efficient.

Gullison, R.E.; Rice, R.E.; Blundel, A.G., 2000, Marketing species'
conservation: Nature, v. 404, p. 923-924.

Hardner, J.J., and Rice, R.E., 1999, Rethinking forest concession
policies *in* Keipi, K., ed., Forest Resource Policy in Latin America:
Washington, D.C., Johns Hopkins Press.

Hardner, J.J., and Rice, R.E., 1994, Financial constraints to
\'sustainable\' selective harvesting of forests in the eastern Amazon:
bioeconomic modeling of a forest stand in the State of Para, Brazil,
*in* Proceedings of Reforma de las Politicas de Gobierno Relacionadas
con los Recursos Forestales en America Latina: United States Agency for
International Development, The World Bank, and the Center for
International Forestry Research.

Jackson, Siobahn, 2001, A review of protected areas budgets in
developing countries: Washington, D.C., Center for Applied Biodiversity
Science--Conservation International Report.

-   This report is part of a set of reports to CI--CABS intended to
    improve implementation of conservation concessions and other
    protected areas. In it, the various cost sources and structures
    involved in creating and managing protected areas were analyzed and
    organized into a systematic framework. Rough estimates of operating
    budgets were derived from surveys of protected areas around the
    globe.

Katzmann, M.T., and Cale, W.G., Jr., 1990, Tropical forest preservation
using economic incentives: Bioscience, v. 40, p. 827-832.

Kremen, K.; Niles, J.O.; Dalton M.G.; Daily, G.C.; Ehrlich, P.R.; Fay,
J.P.; Grewal, D.; and Guillery, R.P., 2000, Economic incentives for rain
forest conservation across scales: Science, v. 288, p. 1828-1832.

Main, M.; Roka, F.; and Noss, R., 1999, Evaluating costs of
conservation: Conservation Biology, v. 13, p. 1262-72.

Montgomery, C.A., and Brown, G.M., Jr., 1992, Economics of species
preservation: the spotted owl case: Contemporary Policy Issues, v. 10,
p. 1-12.

-   This paper attempts to derive the supply curve for---and thus the
    marginal cost of---protection of the endangered species, the
    Northern Spotted Owl. By using expert knowledge and a population
    dynamics simulation model, the authors quantitatively related
    species survival to habitat availability. Then, they calculated the
    opportunity cost of not cutting timber in areas where owl habitat
    either is or potentially could be. They used an econometric model of
    stumpage and wood products to derive this value. The result is a
    supply curve that traces the relationship of probability of
    continued owl survival as a function of foregone timber revenue and
    the ensuing producer and consumer surplus.

Polasky, Stephen, 1998, When the truth hurts: endangered species policy
on private land with imperfect information: Journal of Environmental
Economics and Management, v. 35, p. 22-47.

Witting, Lars, and Loesschcke, Volker, 1993, Biodiversity conservation:
reserve optimization or loss minimization?: Trends in Ecology and
Evolution, v. 8, no. 11, p. 417.

Wong, Carmen, and Gullison, R. E., 2000, Risk assessment and management
of catastrophic threats to tropical protected areas: Washington, D.C.,
Center for Applied Biodiversity Research--Conservation International
Report

-   This paper is a compilation of 64 catastrophic events, both natural
    and anthropogenic, that took place in 32 protected areas around the
    planet. The authors identified trigger events to catastrophic loss
    and the conditions that increased the areas' susceptibility to these
    events. They also describe a three-step process for risk management
    that includes assessing risks, establishing "comfort levels" with
    various threats, and monitoring one or more indicators for the
    trigger events that bring about the dramatic losses.