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8/6/2019 Communicating a Vision - Berlin Aug2000
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Communicating a vision of the future with optimization
models and virtual landscapes: An application tocommunity management of the Jalapa watershed in
Honduras.
Grégoire Leclerc*, Bruno Barbier*, Alexander Hernandez **, Orlando Mejía*.
*International Center for Tropical Agriculture (CIAT), Cali, Colombia.
Corresponding author: [email protected]
**Corporación Hondureña para el desarollo Forestal )COHDEFOR),
Tegucigalpa, Honduras.
D R A F T
_____________________________________________________________________
Abstract
This computer demonstration presents the results of exploratory work on the
communication of possible scenarios outcomes to the population of a small
watershed. This is done by combining optimization models with computer-
generated images of future landscapes.
Five scenarios (a rapid population increase, a sustainable forestmanagement, an increase in agricultural productivity, a new credit program
and a payment for environmental services) were introduced in a linear
programming (LP) model which maximizes the total income of the watershed
while finding the most profitable land use condition.
The results of the model were fed into a virtual-reality (VR) landscape
rendering software that allow to simulate the aspect of the watershed undergiven scenarios. We presented to the population, realistic “pictures” of the
watershed they are living in, according to possible futures. This included
present and future roads, buildings, land use patterns and eroded hillsides.
We also generated animations that correspond to what an observer that
travel trough or fly over the landscape will have in his field of view.
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forecast and discuss the impact of different collective actions such as new
policies, new local rules, or adoption of new techniques on both their incomes
and their landscapes.
Keywords: virtual reality, 3D, landscape, landscape architecture,
optimization, linear programming, Vista Pro, GAMS.
Introduction
Honduras has a long history of problems related with watershed
management. In the late 80's, the El Níspero hydro-electric dam reduced its
operations by 50% due to sedimentation problems (Chavez, 1992). During
early 1994, the El Cajon hydro-electric dam reduced its operation due to high
sedimentation rates and a severe drought that affected this area. The
resulting power shortages cost $20 million each month in loss of industrial
production (Gollin, 1994). In 1998, when Hurricane Mitch hit Honduras,
floods and landslides took more than 11,000 lives. Millions of dollars werelost in infrastructure damages (USGS, 1999). In all cases were, the press
accused deforestation and the general mismanagement of the hillsides by
farmers and loggers as well as the inaction of the government.
There are now many watershed management projects in Honduras financed
by major as well as small donors. These projects dedicate themselves to
reforestation and to community management. Most of these projects use GISto define and promote sustainable land use. Several methods have been
implemented to determine what would be the best land use (Richter 1995).
However, the focus of these methods has been mainly biophysical with little
consideration for socio-economic variables making the recommendations
difficult to apply (Richter 1995).
Most watershed management projects in developing countries aim to increase
productivity while minimizing environmental damages. Win-win situationmight occur but in most cases there is a trade-off between productivity and
environmental conservation. One has to compromise between both objectives.
To calculate such trade-off one need to assess the effect of different options on
the productivity and the environment. Scientists have developed different
tools that help establish such relations These decision support tools have
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utility maximization framework. Thus an optimization model is likely to
predict farmers behavior when conditions are changing. In Honduras like in
most developing countries a watershed is mainly controlled by the decisionstaken by private owners and very little by government. Many conflicts may
occur if watershed management plans are not designed to propose land uses
that maximize economic incomes. It is highly recommended to develop
methods that link the biophysical configuration with the private decisions
(Beaulieu et al., 1998).
Visualizing the landscape
CIAT has been experimenting with many ways to interact with communities
to discuss and plan about the landscape they are living in, and assess the
impact of policies and decisions on the land.
This included paper maps, maps projected from a computer, papier maché
models, maps projected onto a styrofoam model, photomosaics. Also,
participatory mapping exercises where various land use elements are drawnfree-hand by farmers, then georeferenced to relate this local data and
country-level data. In recent years we worked increasingly with large
photomosaics created with orthorectification software; with scanned vertical
airphotos, obtained from national mapping agencies, were could obtain a
capacity to resolve features down to 50cm in size, and farmers were
extremely quick to adapt to this new perspective (i.e. the vertical view), could
easily locate their farm, the road to the village, the church, the football field.Oblique perspective view were generated by draping the orthophoto on onto a
high-resolution DEM created by photogrammetry), which provided the basis
for discussion on collective landscape issues such as water management or
reforestation. Finally we tried with chromostereo (Puig et al, 1997), a color
coding technique that adds a depth perception to photomosaics by means of
low-cost glasses (www.chromadepth.com).
In general terms, this type of work was more a pretext for discussion, wherechromostereo photomosaics and papier mache models were undisputed
champions. However, it was indeed very difficult to introduce the concepts of
exploration of options and the future that would result from these
discussions, because 1) we have no airphotos of the future, and 2) it was
extremely time consuming to paint the papier mache model with new land
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advantage of the GIS. Visual interpretation of land use on photomosaics
were found to be an excellent entry point to the understanding and use of flat
abstract maps.
All these problems and alternative methods have been and are still used in
developed countries, whose population often benefits from a more complete
education who prepares them better to understand and use abstract maps.
Despite this comparative advantage, the trends we observe in developed
countries (for local development, for instance) is exactly the opposite to using
more abstract representations. This is particularly true in architecture,where clients are traditionally confronted, in addition to the abstract plan,
with an artist representation of the future house or landscapes. This age-old
method is being progressively replaced by computer assisted virtual reality
representations with ray-tracing algorithms CAD extensions. More complex
applications are the norm now in urban planning. The mayor will be
presented computer generated representation of parts of the city to support
decisions on a new building, or an allay of trees, or a new park. The trend
again is to improve the visual representation using computer algorithms thatare more and more sophisticated. This trend can be observed in the hardware
development, for example in video gaming industry (Nintendo 64 provides
good examples of ultra realistic scenery).
A notable example of computer-generate realism is provided by a the AMAP
software developed by CIRAD-AMIS, where plant structure databases allow
to simulate their growth and appearance. This results in stunningly realplants at different stages of growth (flowering, losing leaves in winter), that
are used in conjunction with architecture software to create virtual
landscapes to be analyzed by various urban development projects.
In developing countries education levels are generally low, and the contact
with the land more direct than in developed countries. The rationale for
using abstract maps for local development is not clear, though, but often the
rule of thumb is: low-tech is more suitable for low-development. It is clearthat computers, or color maps, are intimidating and may handicap our
attempts for communicating further. But can we honestly discard new
technologies as not appropriate, especially when we can see a clear trends in
developed countries? As we found out, our most expensive and cutting edge
technology was involved in the production of orthophotomosaics a product
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are more random and diverse, and existing software is clearly not
appropriate. Figure 1a shows a typical computer generated landscape for
forestry applications (Thorn et al, 1997). This was unacceptable for realforesters, who needed a more realistic representation to interact with local
residents. The authors opted for an extremely time consuming yet
stunningly realistic manual edition in a photoediting software (Figure 1b).
We found out about a new low-cost software for landscape generation
completely by accident. VistaPro, in its version 4.0, is clearly the result of
enthusiasm and creativity of the development team. It allows to generate
landscapes with stunning realism by exploiting the fractal nature of land,water and clouds, and specific characteristics of geomorphology and plant
growth. It also adds some features found in video games. At 149,99$ (now
69,99$) we didn’t expect a “scientific software” but instead a software with
focus in generating a product for a non-scientific yet exigent user, where
realism if the final product is more important that spatial accuracy or
interface with CAD software.
Since many years, though, image processing software allows to representlandscapes in perspective, with an image optionally draped on the surface.
Recently the leader in GIS software, ESRI (ESRI, 2000), realized the
potential of perspective views and introduced the 3D Analyst, an extension to
its popular ArcView software that allows representation of landscapes with
some superimposed structures. The basis is a TIN model (which should
include buildings if any) on which imagery or features such as rivers and
roads can be draped, resulting in a good yet quite abstract view.
The Study Area
Honduras, located in the middle of Central America, is characterized by a wet
climate in the north and a semiarid climate in the south. Precipitations are
irregular. More than half of Honduras might experience rainfalls greater
than 300 mm in 24 hours (Hargreaves, 1992). Approximately 46% of thecountry is still covered by forests (Rivera, Martinez, and Sabillon, 1999),
while arable land is being used for agricultural and cattle ranching.
Eight of the ten world soil orders exist in Honduras. Most of these soils are
classified with a high to very high erosion risk (Rosales 1994) In addition
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increasing steadily, while water quality is declining at an accelerated rate
(Organization of American States-National-University of Honduras, OEA-
UNAH, 1992; Government of Honduras, 1991).
The Jalapa river watershed is a typical sub-watershed of the highlands of
Honduras. It is a second-order watershed of the upper El Cajon watershed,
which supplies 57 percent of the hydroelectric generated power for Honduras.
The Jalapa watershed has abundant water resources (Hargreaves, 1992).
The annual discharge from the major streams is used for domestic
consumption and livestock but very little for agriculture (Gutierrez, 1992).
The Jalapa river watershed covers an area of 2,509 hectares (Hernandez,
1999) and sustains a population of 2,500. This population is entirely rural
and the vast majority lives below the poverty line. The population density is
around 80 per square kilometer and is increasing at 3 percent per year which
means that the population is likely to double in the next 20 years.
The topography of the watershed is rugged. Tributaries are generallyenclosed in narrow V-shaped valleys and possess a dendritic drainage
pattern. Soils of the upper watershed which originated from limestone are
fertile. Soils of the medium and lower watershed which originated from clay
stone are much less fertile (Simmons y Castellanos, 1968). Only a small
valley in the lower watershed has alluvial fertile soils. Most of these soils,
especially in the upper watershed, present slow water percolation and
therefore high surface runoff (Hargreaves, 1992).
The annual mean temperature varies from 22 to 24 Celsius (Hargreaves,
1992). The climate consists of a dry season (higher temperatures from March
to April) and a rainy season (lower temperatures from May to December)
(Hirt et al., 1989). Rainfall in the valleys is approximately 1200 mm per year,
while upland forest-covered areas receive an annual average of 2000 mm
(Hirt et al., 1989).
Because of good soil quality and abundant rainfall, the upper part of the
watershed is totally cultivated while the middle part is occupied by forest and
a small valley in the lower watershed is mostly left for extensive pasture.
Most of the farming consists of semi-subsistence farming of maize and beans.
Coffee production is limited Agriculture covers about one third of the
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land use map of Figure 3). The pine trees are Pinus oocarpa specie, which is
adequate for timber production. The broadleaf trees are usually deciduous
species of Oak (Quercus sp). This oak is only suitable for fuel wood and fences(Hernandez 1999).
Methodology
Aerial photographs and maps of slopes, altitude, access to market and land
tenure were used to determine Homogenous Land Units (HLU) as inputs to
an optimization model. The model helps determining which combination of
land use options (agriculture, pasture, coffee, pine forest, broadleaf forest)
maximizes the profits of the whole watershed. The land use options are
mapped in ArcView, then exported to VistaPro for visual rendering. Two
products are created with VistaPro: 1) posters of the watershed from different
viewpoints, and 2) flight-through animations.
The data
We conducted a small survey of 8 typical farms to determine a few variables
of economic decisions (prices, costs, performance, requirements and
availability of labor and capital). We selected additional data from the
national agriculture census of 1993 to cross-check the information obtained
from the surveys. We also revised the forest management plan document
from 1996 prepared by the forestry ministry COHDEFOR. The informationobtained included: Forest Stands charts and existences, productivity per ha
per year and per forest type.
Homogeneous Land Units (HLU)
For the purpose of the present study 72 HLU were established. Four
variables and their combinations were considered sufficient (Table 1)
Table 1: Choice variables to determine the HLU
Variable Codes & Description Variables
1 2 3 4
Slope 0 to 15% 15.1 to 30% 30.1 to 45% More than
45%
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A topographic map (1:50,000 scale) with 20-meter contours was digitized to
generate the DEM (Digital Elevation Model) and slope coverage. We digitizedvisible roads based on a orthophotomosaic of the area and verified with GPS.
We created the distance to roads buffers using Arc View Xtools extension.
The information about land tenure was digitized from cadastral maps
(1:50,000 scale).
The four coverages (slope, altitude, distance and tenure) were spatially
overlaid, which resulted in 72 combinations that would become the
Homogenous Land Units HLU (figure 2).
Each HLU has a different productivity based on data from the 1993 national
census (DGECH 1994). Productivity is higher at higher altitude and on
flatter areas. Labor time is dependent on the distance to the road.
The model is validated by comparing the land use generated by the model
with the current land use produced by visual interpretation and on-screendigitizing on a orthophotomosaic (Figure 3). The interpretation was further
refined with GPS ground truthing.
Optimization model
The optimization model maximizes the income of the whole watershed like it
was one large farm (central planner viewpoint). The different competingactivities are the different land uses. The limitations of the model are land,
labor, capital, and consumption. Land is divided by the 72 HLU. Labor,
capital and consumption are allocated for each of the three elevation ranges.
Transport time is included in the labor requirement of each HLU. The model
is static since we are looking at average year not at the initial investment.
We do not consider investment time in this exercise.
The results of the calibration run show what would be the optimalcombination of land use for the whole watershed under current conditions.
The results are usually close to what farmers already do. This is normal since
the model reproduces the main constraint of the farming system. Then we
can use the results in a descriptive way, predicting what is likely to happen
under different conditions
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Characteristics of Vista Pro version 4.0.
This section details the functionality of the VistaPro software and of itsoperation. Figure 4 shows a snapshot of the main screen. The square
window to the top left contains a representation of the DEM, with the
position of the camera and target, as well as the field of view. The bottom left
window provides a draft overview of the image to be rendered. A series of
tabs to the right give access to the various processing steps
Camera and target, viewpoints.
There is a lot of flexibility in defining the position of the eye (the camera) and
where we are looking (the target), either using the mouse or entering
coordinates directly. The camera has a variable focal length. It is possible to
restrict the motion on one dimension (such as the elevation). Scenes can be
rendered at images sizes up to 1280x1024.
Terrain and geomorphological features: DEMs fractals, cliffs, valley effects.
The terrain is represented by a grid (for calculations) or a tin or wire frame
(for preview). It is possible to import a USGS DEM as well as a 16-bit binary
grid (where each pixel has the value of the elevation at that position), but this
is something only a GIS expert can do. Note that ArcView can only create a
8-bit binary grid directly so the user may have to rely on other GIS software.
However, the VistaPro distribution disks contain programs to help convertascii files (e.g. random or ordered X, Y, Z points) into a 16-bit binary grid for
import.
The default pixel size is 30m (USGS default), and although this can be
changed we found that it was better to work at 30m resolution because forest
density ranges was better adapted to that resolution. Another easier way to
impost a DEM is via a grey scale PCX file, but then coordinates will be
unknown. Vertical exaggeration can be adjusted by adjusting the imagecontrast before importing. Maximum size for the DEM is 4098x4098, but we
found that 514x514 was acceptable and less demanding on memory and
hardware requirements. Other possible landscape size are 258x258,
1026x1026, 2050x2050. DEMs can be smoothed by VistaPro while imported.
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apparent (its color can be changed). Also, when slope is too steep (before it
becomes a cliff), trees density will be automatically set to a lower value. A
very important feature of the software is a fractal small-scale structure thatis superimposed on the coarse resolution DEM. Roughness levels can be
adjusted to obtain the most accurate representation. Use of a more precise
DEM is not necessary with fractals. Finally, a “valley effect” can be added, to
represent the effect of micro climates in valleys and attenuate the change
between valley bottom and foothills.
Finally, the landscape can be eroded or smoothed.
Water: periodicity and fractals, running water effects
.
Water effects are dramatic. Rivers, Lakes, ocean can be created based on the
DEM. Water can be made periodic, in addition to fractal small-scale
structures. Its color can be changed. Specular reflection of light on the
periodic/fractal water surface is extremely well rendered. Shallow water issemitransparent. One of the authors who was attending a workshop in a
Nicaragua resorts on the pacific coast two weeks after receiving the software,
could hardly tell the difference between the actual real sunset he could
observe on the ocean and a demo video of a coastal area (complete with palm
trees) on the VistaPro distribution disks. Rivers representation includes a
flare that depends on the slope (simulates the speed of running water).
Waterfalls correspond to water on vertical slopes, are automatically renderedas such. All these semi-automatic “intelligent” features make the job of
creating a realistic landscape a lot easier.
Placement: plants and man made features. 3D and textures. Manual vs
automatic
Forest, buildings, roads, sand and water can be manually added to the
landscape using the mouse. However, we would recommend using a low-costdigitizing tablet such as the 99$ Pablo (Pablo ref). Forest composition can be
set-up to more accurately represent the actual forest: tree size, color of trunk
and crown, composition and density. The choice is limited, though, to Oaks,
Pines, palms and cactus. Trees can be drawn in 2D (with a texture draped on
them if needed) or in 3D where each leave or branch is drawn individually
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Users have the choice of one and two stories buildings, as well as small office
and tall office buildings. Textures can be draped on the buildings for
increased realism.
To better take advantage of textures for trees, terrain and buildings, the user
could take pictures of actual trees, walls, roads in the study area and use
them as texture maps in addition.
A poorly documented feature consists in importing a PCX file to represent
objects to be placed on the landscape. With trial and error we were able to
construct a correspondence table between PCX color index and a few land
covers. We have been trying (with no success) to obtain technical support
from the company (www.romtech.com) to take full advantage of this feature.
Light and sky. Clouds, sun, moon, shading. Reflection of surfaces (water,
land, vegetation).
Lightning conditions are also essential for bringing realism to a scene. The
software accurately mimics the path of light and creates a range of reflections
on surfaces, from specular to diffuse. In addition, one can add fractal clouds
of variable density and altitude. A sun or a moon can also be positioned in
the sky, and their color as well as the color of ambient light changed.
Animations and stereo.
Animations allow to explore the landscape dynamically. This is more
oriented to video gaming, where a user can select a vehicle type (glider,
motorbike, helicopter, dune buggy, or custom where bank, heading and pitch,
can be adjusted manually), create/edit a path over the landscape, and
generate a video. Creation of animations is extremely computer intensive
since 10-15 frames have to be generated for every second of animation, butthe product very entertaining. Another feature of the software, that we have
not used yet, lies in the capacity to generate stereo pairs, for still frames as
well as for animations. The pairs, when observed using a stereoscope allow
to gain amazing perspective.
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the DEM can be imported into VistaPro from a flat binary integer file, which
preserves the elevation value, but ArcView cannot produce this format. We
would not recommend to a non techy person to try this at home. Our DEMwas in a unix workstation, and we had to swap the bytes of the integers in
order to use them on a PC. We didn’t try USGS format. The easiest way that
we found was to open the DEM as an image (not a grid) in ArcView, and
adjust the contrast to obtain the vertical exaggeration needed, which may
take a few trials. It is important to keep the pixel size to approximately 30m.
To do so, we recommend opening a view with approximately 600x600, and
zoom to a value that will lead to a pixel of 30m. The zoom value can be
computed as follows:
Z = 30 x hres / hsize
Where hres is the horizontal video card resolution (e.g. 1024) and hsize the
horizontal dimension of the screen (in meters). For example, a scale of
1:90000 is appropriate for a 21” screen (16.5”horizontal) with a video card
resolution set to 1280x1024.
Another point to have in mind is to have a DEM that covers an area larger
than the area of interest (about twice is OK). If the DEM is adjusted too
tightly to the AOI, we end up with the impression that the AOI is floating in
a flat land.
ArcView can only generate JPG files which are compressed and distort the
vertical scale. So it is better to use the Prntscrn key of windows, then create
a 256-colors PCX of dimensions 514x514 with a graphics software. Since
VistaPro accepts only 256 colors PCX, conversion from a larger color palette
has to be done carefully in order to preserve color indexing. Once the
ArcView view has been set-up, the project should be saved at this stage: the
land use maps (for diverse scenarios) have to be captured identically if we
want them to accurately match the DEM.
As mentioned above, we could obtain by trial and error a correspondence
table between a PCX color table and a few land cover classes. We could
associate a PCX color index to: pure stands of pine (index 15), oak forest
(index 14), palm trees (index 13) and cactuses (code 12), houses (all types-
indexes 28 to 31) bare soil (sand index 8) and water (index 2); also we
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VistaPro via a PCX format file. Before we capture the screen, the DEM is
turned off, and Roads and rivers are turned on over the land use of interest.
Note: since ArcView do not allow any control of the color index, we had to useseparate software to assign the correct code to the land use. We used an
image processing software, but any raster GIS would do. For reproducibility,
a land use legend is setup in ArcView with pure colors (e.g. red for pasture,
blue for rivers). Pure colors will not be dithered when transformed to PCX.
The image produced by capturing the screen is cropped to 514x514, recoded
in an image processing software, then converted to 256-color PCX. This
process is a bit tricky at first, but ultimately takes about 3 minutes to
complete.
Graphics software such as Paintshop Pro can also be used but then color
indexing is controled through the palette: in ArcView, a given land use will be
assigned a color identical to the one corresponding to the PCX palette color
index. Once we obtain technical support from Romtech, we will be able to
automate these steps.
Performance.
Performance of VistaPro is excellent, but can quickly degrade depending on
the degree of detail we need for 3D trees, and –more importantly- the number
of trees appearing in the rendered scene. The software is not demanding in
term of windows resources, and calculations in the background do not affect
other applications. Each landscape shown in Figure 5 (1280x1024) takes
between 10 and 30 minutes to render on a 800Mz Pentium PC. Trees were
drawn in 3D with a high level of detail. A 30 seconds video with 800x600
frames takes between 12 and 36 hours generate on the same computer. The
more deforested the landscape, the faster the rendering time. A scripting
language allows to automate a lot of operations for batch processing.
Limitation of the software.
VistaPro documentation is poor and we are still waiting to receive a reply to
our questions from Romtech technical support. We would appreciate a more
complete library of plants and better control in importing land cover maps.
DEM resolution is restricted to 30m, the software is not well adapted well to
other resolutions Manuel positioning of landscape elements is difficult to do
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Results.
Actual land use.
Generated landscapes for actual land use are shown in figure 5a. While
trying to reproduce as close as possible the actual landscape, the results is
still not entirely accurate, although infinitely better than the flat GIS map
(Top left of Figure 5a). Still, it seems that some degree of symbolism is
necessary to communicate better. For example if we want to display a large
watershed in its totality in one rendered scene, the trees may be too small to
allow the type of forest to be appreciated. We may want to generate trees
slightly taller than nature, or wider rivers and roads so they can be seen even
from a distance. Forest color can be slightly altered to improve contrasts.
The main decision we took on that respect was to represent basic grains
agriculture as bare soils, while agriculture is more a patchy landscape of bare
soil, crops, fallow and guamiles. Although we could manually edit this land
use to give it a more natural appearance, we wanted to improve contrast
between land units, and emphasize the effect of soil erosion, whichaccompanies all agricultural activities, as a main determinant of
sustainability.
Farmers reacted favorably, slightly amused but enthusiastic, before this
“allegoric” representation of the landscape they live in, that they could
observe for the first time from points not accessible by road.
Optimum land use under current conditions.
Figure 5b shows the land use generated by our model, under the conditions
that apply currently. We did not expect the map to coincide exactly with the
reality (Figure 5a) but one can observe that they are quite similar, i.e.
agriculture in the upper part of the watershed and the valley, forest in the
middle part. By analyzing where the results differed with reality, we can
improve our model.
Sustainable forest scenario
There is an indigenous tribe living in of the watershed that owns a large area
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Environmental payments scenario
Payment for environmental services is becoming common in Costa Rica for
carbon sequestration or in Colombia for sedimentation reduction. We
simulated the payment for carbon sequestration, water production and
erosion control. The model suggests to leave the upper part of the watershed
in fallow to produce more water while reducing erosion. The resulting map
and generated landscape are displayed in Figure 5c. This scenario was
perceived by farmers as one that will never take place in the future.
However, the Jalapa watershed is part of the lager El Cajon watershed where
payments are being now given to farmers to reduce sedimentation in the
most important reservoir of Honduras.
Reaction of farmers and best practices.
The model is a central planner model. The model makes the assumption thatfarmers will collaborate coming close to what would be a perfect market. This
assumption is sometimes strong in Latin America. Results are not really
predictive.
We found that large posters representing a large portion of the landscape
from different viewpoints were an excellent way to initiate discussions with
farmers about optimization issues in their landscape. Farmers found
animations more entertaining than really useful. However we realized that
animations were helping farmers go smoothly from the viewpoint they are
familiar with (i.e. ground level) to a viewpoint from a higher altitude. Photos
taken from high points (this is possible in the hillsides) were also used in
conjunction with landscapes generated from the same viewpoint and farmers
could make the connection with the more abstract computer-generated
landscapes.
Farmers responded favorably to the scenarios, however most mentioned that
the situation portrayed would never happen in reality. This was the starting
point of a discussion where we could explain that there was, 50km from the
Jalapa watershed, a large project where farmers were paid to work for soil
and water protection in the El Cajon watershed which the Jalapa watershed
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Finally, we found that although LP provides an optimum solution, there will
always be a need for some minor manual edition to adjust for particular
situations, add or remove a road segment, a few houses, a small patch of forest. This is better done directly in VistaPro with a low-cost digitizing
tablet (such as the 100$ KidsWork Pablo’s – www.kidswork.com) that
provides much better positioning control that a mouse.
Future work.
Our experience was sufficiently conclusive to justify more work in the area.
This consists in three stages.
1) automate the import of DEMs and land Use maps. In this case, we seek
technical support from Romtech to help identify the whole range of land
use types that can be imported directly and the way they are coded in
VistaPro.
2) Improve the reality of rendered scenes. This involved better use of the
features of VistaPro’s fractals, colors, and textures. However, we willalways be limited to 4 plant types and the range of available plants
cannot be increased without considerable investment. It is possible to
contact the VistaPro programming team to discuss the possibility of
adding an option for users that allows setting-up the characteristics of
new plants. Another option is to include some of VistaPro features into
AMAP. Or to convince ESRI to invest in what we believe is the future of
GIS: rendering of photorealistic landscapes.
3) Test systematically with farmers and measure what is necessary for best
representation, including stereoscopy.
References.
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COHDEFOR(1996). Plan de acción forestal de largo plazo 1996-2015.
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Cajon region. University of Pittsburgh- Instituto Hondureño de
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Rivera, S., Martínez, L. M., and G. Sabillón (1998). Multitemporal analysis
of the deforestation in Honduras Watershed Science Unit, Utah State
University. 25p. (Unpublished, in prep.)
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sobre los suelos de Honduras. Programa de las Naciones Unidas para el
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Thorn, AJ, Daniel, TJ and Orland, B, Data Visualisation for New Zealand
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Figures.
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Figure 5a. map of current land use (top left) rendered as seen from the south of
the watershed.
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Figure 5c. Map of sustainable forest scenario (top left) rendered as seen from the
south of the watershed.
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Figure 5d. Map of payment for environment services scenario (top left) rendered
as seen from the south of the watershed.