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.

mailto:[email protected];





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



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



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



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



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



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%



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



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.



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



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.



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



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



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



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



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.

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COHDEFOR(1996). Plan de acción forestal de largo plazo 1996-2015.

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ESRI, 2000. Environment Systems Institute, Redlands, CA, USA.

Gollin, J. D. (1994). Trees down, lights out in Honduras. Habitat. The

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Figures.









Figure 5a. map of current land use (top left) rendered as seen from the south of

the watershed.





Figure 5c. Map of sustainable forest scenario (top left) rendered as seen from the

south of the watershed.



Figure 5d. Map of payment for environment services scenario (top left) rendered

as seen from the south of the watershed.

Documents

Communicating a Vision - Berlin Aug2000