Application of Remote Sensing and GIS
for Forest Cover Monitoring in Lao PDR

EXECUTIVE SUMMARY

This report is prepared for the GIS workshop at Hanoi, Vietnam. It describes the activities of the regional Forest Cover Monitoring Project (FCMP) in Lao PDR with the main focus on the methodology of forest cover monitoring processes.

The FCMP was initiated by the Mekong River Commission (MRC), co-financed by the Government of Germany and implemented through the Mekong River Commission Secretariat (MRCS) with assistance from the German Agency for Technical Cooperation (GTZ).

All Lower Mekong Basin (LMB) countries that are members of the MRC, i.e. Cambodia, Lao PDR, Thailand and Vietnam, have been facing rapid destructions of their forests over the past decades with serious consequences for the quality and function of the entire LMB watershed and the livelihood of its rural populations.

The FCMP was initiated because the MRC became aware of the need to generate and collect recent and reliable information on the current status of forest cover and on the location and intensity of its degradation and destruction and, as far as possible, on the socio-economic conditions leading to them. This information was to be generated and collected in order to provide decision makers and planners in the national planning ministries and agencies of the MRC member countries and in the regional MRC itself with a sound decision basis to formulate adequate policies and strategies to preserve the remaining forest cover.

The visual interpretation technique is applied to extract the information on the current status of forest cover and on the location and intensity of its degradation and destruction (monitoring information) from hard copy satellite images at 1:250,000 scale acquired in 1992/93 and 1996/97. For the process of monitoring forest cover changes, the so-called interdependent interpretation technique is used. This produces much more reliable monitoring result than the comparison of two independent interpretations. The result of the interpretation was then intergrated into a computerized Geographical Information System ( GIS ), by which different products e.g. forest cover statistics, forest cover change statistics, maps, were analyzed and prepared.

To ensure the homogeneity of the forest cover information in all FCMP countries, the new forest and land cover classification system, which was relevant to the watershed management issue, was developped and used in all four FCMP countries. The threshold of 20% of crown density is used to differentiate between forest and non-forest areas.

Supplementary information on forest composition was extracted from the national forest inventories of the MRC member countries. Information on the socio-economic conditions leading to forest degradation and destruction as far as available was compiled from national census data and other statistics. These data were integrated into a computerized numerical data base which was linked to the GIS.

At present, there are two sets of forest cover data which were generated by two separate institutions e.g. the Lao National Forest Inventory ( NFI ) and the MRC Forest Cover Monitoring Project ( FCMP). Differences between the statistical results of FCMP and the Lao National Forest Inventory (NFI) could be explained and resolved through a study jointly conducted by former NFI and FCMP staff. The results of this study are also discussed in this report.

The German Ministry of Economic Cooperation and Development and the MRC have agreed to fund 2 more years of FCMP post-project support (1999-2000) through the Sustainable Management of Resources in the Lower Mekong Basin Project (SMRP). The post-project support is provided in order to facilitate the distribution and marketing of the FCMP results to a wider range of users, to promote the utilization of the FCMP results in a wider range of applications, to further develop the built up technical skills and experiences, and to introduce new technological concepts and techniques such as digital satellite image processing.

TABLE OF CONTENTS

Executive Summary

List of Abbreviations

Fcmp – Rationale, Objective

Technical Approach – Forest Cover Monitoring

Comparison of NFI and FCMP Results

Further Development of Technical Skills and Experiences

Future Technology Development

List of Abbreviations

FCMP - RATIONALE, OBJECTIVES

FCMP Rationale

In watershed management, healthy, natural forests are essential for the stabilization of the hydrological cycle during the wet and dry seasons. Through their horizontal and vertical structure, ground vegetation and subterranean root system, forests alleviate the impact of torrential rains, absorb excess quantities of rainfall and slowly feed the water into the groundwater table and river systems during the dry season. This results in the continuous flow of water for drinking, irrigation, fisheries, river transport and hydropower generation. Forests also fulfill a variety of other functions, which are of paramount importance for the livelihoods of rural populations, such as protection against soil erosion, production of timber and of numerous non-timber forest products, such as rattan, fibres, dyes, honey, fruits and medicinal plants.

The LMB has been covered by a variety of forest formations. These forests sheltered human, animal and plant communities. They have been used sustainably by the local populations for millennia. Only in the course of this century these forests have become subject to rapid and serious degradation and destruction. It has been estimated that between 1950 and 1970 nearly one half of the forest cover disappeared.

A major cause of forest degradation and destruction is the exponential population growth of recent decades resulting in socio-economic imbalances, which cause pressure from landless farmers encroaching on forest land and converting forests by unsustainable shifting cultivation practices on steep slopes. Furthermore, uncontrolled logging and lack of post-harvest management are rapidly degrading and destroying the forests. Forest degradation and destruction in turn further aggravate the problems of securing the livelihoods of the still growing rural populations.

Serious consequences of forest degradation and destruction have become manifest in all LMB countries. The reduction of forest cover has resulted in decreased water retention potential, increased frequency and intensity of flooding and landslides, loss of soil fertility and agricultural productivity, soil erosion, siltation of reservoirs and intrusion of saltwater into the Mekong delta.

Any LMB-wide policies and strategies to preserve the remaining forest cover would have to be based on recent and reliable information on its current status and on the location and intensity of its degradation and destruction. However, such information was not available at a LMB-wide scale in the early 90s. The last LMB-wide survey of forest cover had been done in 1972 and had only provided very general information on the status of the forest cover, but not on the location and intensity of its degradation and destruction. Some forest cover information was available in the early 90s in the LMB countries, however, this information was also partly outdated and rather inhomogeneous.

Equally, sound information on the socio-economic conditions leading to forest degradation and destruction would be required for any sound planning approach. This information, like the information on forest cover, was only partly available in the early 90s.

The Mekong River Commission, as a regional level planning agency, therefore took the decision in the early 90s to initiate the Forest Cover Monitoring Project (FCMP). This project would generate and collect information on the current status of the forest cover, on the location and intensity of its degradation and destruction, and, as far as possible, on the causes thereof.

Remote Sensing and GIS are the most modern technologies which have been widely used in the field of natural resource management and monitoring. These technologies provide very powerful tools to observe and collect information on natural resources and dynamic phenomenon on the earth surface, and ability to integrate different data and present the data in different formats. Through the use of this technology, the most recent and reliable information could be provided to decision makers and planners in the national planning ministries and agencies of the member countries and in the regional MRC itself, to help them formulate adequate policies and strategies to preserve the remaining forest cover.

FCMP Objectives

Five major outputs had been specified for phases I and II of the project:

1. Establish a Natural Resources Information System.
2. Establish a Forest Cover Monitoring and Trend Analysis System.
3. Enhance cooperation between and within the national and regional planning agencies of the riparian countries and the MRC.
4. Provide training to national planning agencies.
5. Promote the use of project results and capabilities in forest and environmental policy and planning decisions.

It has to be kept in mind that the technical outputs (1. + 2.) were intended to be used at the macro planning level, that is at the regional, national, and at most at the provincial level. It was, during the project’s lifetime, never intended to generate results usable beyond the provincial level.

TECHNICAL APPROACH - FOREST COVER MONITORING

General Approach

The technical outputs of the FCMP were defined as follows:

1. Establish a Natural Resources Information System.
2. Establish a Forest Cover Monitoring and Trend Analysis System.

Technically, a Natural Resources Information System as well as a Forest Cover Monitoring and Trend Analysis System are computer based Geographic Information Systems (GIS) with mapping and data base components. In principle, a Natural Resources Information System contains information on the status of a natural resource (such as forest cover and composition) at a given point in time. In contrast, a Forest Cover Monitoring and Trend Analysis System contains information on the status of this natural resource at several (at least 2) given points in time. In addition, the latter contains information on external factors that influence the natural resource, such as socio-economic and bio-physical information. This said, one can combine the above 2 outputs into 1 output as follows:

This output comprises 3 major layers of information:

• forest cover (e.g.: Where is forest? How many hectares?)
• forest composition (e.g.: Species composition and volume within the forest?)
• socio-economic / bio-physical (e.g.: What is the population density and growth?)

The most important of these 3 layers under MRC’s point of view of watershed management is the first. It is essentially a map layer. The latter 2 layers are tabular information linked to the map layer.

Given the FCMP’s regional character, it was evident for technical as well as for financial reasons that the information contents of a Forest Cover Monitoring System had to be limited in terms of data collection intensity and resolution or scale.

The first issue to be resolved was whether information should be generated or collected from existing sources. Information generation is usually more expensive than collection from existing sources. The FCMP countries together with the MRC decided to use the following approach:

• generate forest cover information
• collect forest composition and socio-economic / bio-physical information

The decision to generate forest cover information was taken because the information available in the FCMP countries was rather inhomogeneous and partly outdated. The information had to be standardized to a certain degree in order to be usable for the purpose of monitoring as well as in order to make it usable for MRC’s basin development planning activities. This standardization could not be achieved using available information. It was therefore decided to spend the major part of the limited funds on the generation of forest cover information as the most important information layer.

On the other hand the FCMP could collect information on forest composition and socio-economic / bio-physical information since this information was available in the FCMP countries at least to an acceptable degree of standardisation. This information was collected and integrated in a computerised database, which was then linked to the information on forest cover. This database will not be discussed any further in this document since it is a state-of-the-art database and contains the data layers of secondary importance.

The second issue to be resolved was as to how to generate the forest cover information. Terrestrial mapping was out of question due to time and budget contraints, as could be expected in a regional project covering 4 countries. Mapping from aerial photos or high resolution commercial satellite images like SPOT (as used in the NFI of Lao PDR) was ruled out for the same reasons. That left only Landsat TM or IRS satellite images as mapping options. FCMP decided to use Landsat TM.

The third issue to be resolved was whether to use digital (computerized) or printed images for the generation of forest cover information. Advantages of using digital images would have been the possibility to do digital image enhancements and classifications, which are not possible with printed images. The main disadvantage of digital images were their high costs of around US$ 3,000 each as compared to US$ 1,000 for a printed image. In addition, the hard- and software required for processing of digital images came at a very high price at the time FCMP started up. Cost aspects are of paramount importance in any kind of information generation, especially when taking into consideration that monitoring requires the repeated purchase of images covering the same area. Another disadvantage of digital images is that their processing requires staff with a least basic experiences in operating computer systems which are far more demanding than normal office applications. Such experienced national counterpart staff were not available at the time the project started. FCMP therefore decided to use printed images at a scale of 1:250,000 (a similar approach as used in the NFI of Lao PDR).

After selecting the appropriate data source for the generation of forest cover information, the FCMP country teams together with the TSU of MRCS jointly developed and agreed upon a forest and land cover classification system. The classification system as the core of the FCMP’s technical output will be discussed in greater detail below.

The classification system was designed to be used in all 4 FCMP countries to ensure standardization and homogeneity of the to-be-generated forest cover information, the importance of which has already been discussed above. The original concept was based on previous experience in the target countries, drawing particularly heavily on the NFI SPOT satellite image interpretation exercise conducted in Lao PDR During development of the classification system, numerous field trips in all four MRC project countries (especially in Lao PDR) were undertaken to carry out practical testing of its applicability.

Once the forest and land cover classification system was established, the satellite images were then accordingly visually interpreted. In Lao PDR this interpretation was done by national image interpretation specialists, who had already done SPOT image interpretation during the NFI. Field trips were undertaken repeatedly during the image interpretation to appropriately ground truth the information.

Aerial photos were used in all four countries to support and verify the satellite image interpretation. In Cambodia, Thailand and Vietnam, existing photos could be used. In Laos, however, new aerial photos were taken by the FCMP in 1993 / 94, since existing photos (early 1980s vintage) were considered seriously outdated. These photos were taken in strips distributed across the whole country and covered all major vegetation types in Lao PDR

Two satellite image interpretation rounds were carried out, the first with satellite images from 1992 / 93, the second with satellite images from 1996 / 97. The second interpretation round was designed and carried out as a so-called dependent interpretation. A dependent interpretation means that the results of the second interpretation round are based on those of the first. In this process, corrections are applied whenever interpretation errors from the previous interpretation round are found. ‘Dependent interpretation’ produces much more reliable monitoring results than a comparison of two independent interpretations. In the latter case, the results of the first interpretation are not known during the second interpretation round so that the (usually numerous) errors made during the first interpretation round are largely overlooked.

Image interpretation (including, in Laos a limited number of Landsat MSS satellite images from 1974/75/76 which helped provide information on forest cover development trends during the past 20 years) was followed by inputting the interpretation results into a GIS. The GIS system used was a relatively simple PC-based ArcInfo / ArcView system. This technical approach was choosen because existing technical staff in both the Forestry Departments of Lao PDR and Thailand were already using PC ArcInfo, whereas GIS technology was completely new to the Forestry Departments of Cambodia and Vietnam.

The forest cover monitoring (change) information was produced by comparing (overlaying) the computerized results of the first (1992/93) and second (1996/97) interpretation rounds. The monitoring process is outlined in the following figure.

Forest and Land Cover Classification System

The Forest and Land Cover Classification System is the core of the FCMP’s technical output and may have led to differences between previously compiled national figures and the FCMP results. Therefore it is discussed in greater detail in this chapter.

The general user requirements and information needs to be met by the classification system as well as its limitations were defined prior to designing it as follows:

• The thematic and the spatial accuracy have to be sufficient for conclusions at a Sub-Regional Level. It is not intended to meet all requirements at the local planning level.
• The Forest and Land Cover Classes should be relevant for watershed management issues such as erosion risk, soil protection and others.
• Repetition of interpretation and mapping as part of a Monitoring System must be feasible in a timely manner at reasonable cost.

• The classification will focus on Land Cover classes, not on Land Use classes.
• The forest classes will represent broad Forest Cover types. The classification cannot offer sufficient accuracy and detail for local forest planning where parameters like forest type, forest structure, species distribution and detailed crown cover percentages have to be described.
• Class separation can be difficult in Transition Zones between two classes. Class boundaries will not have an accuracy of more than ± 250 meters.
• Small Patches of Forest may remain undetected due to limitations of the source data (satellite images) and other effects. While this can be accepted at the sub-regional level, it is understood that at local level these patches may add up to an important forest area.

Forest and land cover classes were defined on the basis of previous experiences made in the FCMP countries and on forest definitions and formations as described in the relevant literature. As far as possible internationally accepted criteria of what constitutes a forest were used (FAO, IUFRO, UNESCO). Limits of what vegetation type can be considered as forest were defined on the basis of the density and continuity of the existing tree cover. Special consideration was given to qualitative changes of the forest cover, e.g. changes in canopy density resulting from logging or shifting cultivation practices. Specific forest cover types, e.g. Inundated Forests around the Tonle Sap Lake in Cambodia or Mangrove Forest in the coastal zone were recognized.

The forest and land cover classes resulting from the integration of the various attributes and criteria as described below were considered as an adequate representation of field conditions and as discernible on Landsat TM satellite images at a scale of 1:250,000.

Any percentages and thresholds discussed below used to distinguish classes and to define their boundaries were provided as guidance for visual interpretation and have to be considered approximate. Precise measurements are not possible on Landsat TM satellite images but would require aerial photos. Therefore the final class boundaries vary in a certain range above and below any defined percentages and thresholds.

The Forest and Land Cover Classification System developed and applied by FCMP is a system designed solely to map Current Forest Cover or, more generally speaking, to carry out an Inventory of Existing Natural Resources. It does not contain or relate to any Legal Definition of Forest Land. Certain countries’ legal definition of ‘Forest Land’ reflects an official view of where forest land should be which does not necessarily reflect where trees actually exist (e.g., areas on which not a single tree grows may be considered as Forest Land). The FCMP results can therefore not be used to make statements on the legal status of any area mapped.

Crown Cover refers to the density (percentage) of the crowns of woody plants above a certain height (ususally 5 - 10 meters). This height threshold needs to be introduced in order to exclude vegetation types formed by woody plants like shrubs or tree seedlings and saplings, which can also reach considerable densities, from being classified as current forest.

Different Crown Cover Classes as seen on the ground

The definitions of Forest and Non-Forest as seen on the ground used by FCMP were as follows:

Crown Cover >= 20 % and

• Tree Height >= 10 meters

• Crown Cover >= 20 % and
• Tree Height 5 - 10 meters

• Crown Cover < 20 % or
• Crown Cover >= 20 % and Tree Height < 5 meters

The 20% Crown Cover threshold was chosen in view of the Dry Dipterocarp Forests, which are abundant in the LMB and which by nature are quite open. For Evergreen and Mixed Forests a 30% threshold might be more appropriate, but would result in the exclusion of major areas of Dry Dipterocarp Forests from the class Forest if applied as a general threshold. A 10 % threshold as used by FAO and developed in view of the, by nature, very open African Woodlands appeared to be rather low for the forests in southeast Asia.

Given the still rather low threshold of 20%, the areas considered as forest by FCMP include a significant amount of severely degraded forest. Any summary statistics of forest cover produced by FCMP must be interpreted with this fact kept in mind.

The class Forest Regrowth was introduced in order to distinguish between mature and regenerating forest (e.g. after an area has been cleared during a commercial logging operation or in a slash and burn farming system).

It is essential to note that the definitions given above are for distinguishing Forest and Non-Forest as seen on the ground. They can not be directly applied to satellite image interpretation without further modification. Nevertheless, they are indispensable in order to come to an understanding of what has been classified as Forest from satellite images.

It is evident that single trees can not be detected on Landsat TM or comparable satellite images. Therefore neither precise Crown Cover nor tree height measurements can be obtained from these satellite images. For precise measurements aerial photos would be required. However, the color and texture of vegetation as seen on satellite images provide information about its composition and structure, which an experienced interpreter can relate to broad classes of Crown Cover and tree height.

Given these restrictions, 3 Crown Cover Classes as seen on satellite images were distinguished in the FCMP Classification System:

• 0 – 19 % (Low)
• 20 – 69 % (Medium)
• 70 – 100 % (High)

These thresholds are estimates rather than measurements. It should therefore be noted that

• Relative differences of Crown Cover will be detected rather than exact thresholds.
• In mountainous areas the appearance of forest on satellite images is variable due to transitions in forest types and site conditions and especially due to illumination and shadow effects. Separation of Crown Cover Classes may therefore be more difficult in these areas.

Optical satellite images such as Landsat TM can only be taken in the cloudless, that is the dry season. During the dry season different Crown Cover Classes of Deciduous Forests cannot be interpreted reliably. The crown density of most natural Deciduous Forests is in the Medium Class. The crown density of natural Dry Dipterocarp Forests even ranges at the lower end of the Medium Class. All Deciduous Forests have therefore invariably been classified as Forests with Medium Crown Cover.

Qualitative changes of the Crown Cover of forests can be mapped from Landsat TM satellite printed images if the Crown Cover changes are significant and recent, the forests and the topography of the terrain are more or less homogeneous and if the images have been digitally enhanced prior to producing the prints. Mapping of selectively logged areas is therefore almost impossible, particularly when the gaps are filled quickly by secondary vegetation.

The Canopy Density Concept has to be introduced in order to finally arrive at the definition of precisely what FCMP considered as Forest or as Non-Forest as seen on satellite images.

In visual interpretation and mapping there is always a tradeoff between mapping accuracy and processing speed. If an interpreter has to map too much detail, he may not be able to complete his task in good time. Therefore usually a Minimum Mapping Unit (MMU) is defined as the smallest unit to be mapped by the interpreter. An internationally recognized standard for the MMU in forest and land cover mapping is 4 × 4 mm at source scale. FCMP originally intended to apply this standard. However, during practical interpretation and mapping work, it was even lowered to about 2 × 4 mm in order to not miss too many small structures.

At the scale of the Landsat TM satellite images used by the FCMP (1:250,000), 2 × 4 mm are equivalent to an area of 0.5 × 1 km or 0.5 km². Given the heterogeneous forest and land cover, which is to be found in major parts of South-East Asia, a MMU of 0.5 km² may contain forest and other land cover types at the same time. All features smaller than the MMU therefore have to be assigned to or grouped into an appropriate class. This makes it necessary to introduce the term Forest Cover.

Forest Cover is the percentage of areas within a MMU where the Crown Cover is >= 20 %.

Forest Cover as seen on the ground (~ 60 %)

Forest Cover as seen on satellite image (Forest in black)

Canopy Density as Combination of Crown Cover and Forest Cover

Canopy Density is defined as the combination of Forest Cover and Crown Cover. Forest Cover and Crown Cover are the deciding factors for the assignment of any MMU as seen on satellite images to 1 out of 3 Forest Canopy Density classes or to the Non-Forest Class. These classes are:

Classification of MMU using Forest Cover (FC) and Crown Cover (CC) Minimum Thresholds
(CC to be estimated within FC only)

Examples

To be classified as Forest, High Canopy Density, a MMU must have a Forest Cover of at least 90 % and this Forest Cover must have a Crown Cover of at least 70 %.

A MMU is classified as Forest, Low-Medium Canopy Density, in any of the following 3 situations:

• Forest has small gaps in it (Forest Cover ³ 70 % but < 90 %), but is otherwise dense (Crown Cover >=70 %).
• Forest has no gaps in it (Forest Cover >= 90 %), but is open or disturbed (Crown Cover >= 20 % but < 70 % )
• Forest has small gaps in it (Forest Cover >= 70 % but < 90 %) and is open or disturbed (Crown Cover >= 20 % but < 70 % )

The areas A – F from the above figure, assuming that the Forest Cover has a Crown Cover of >= 70 %, would be classified as:

Assuming that the Forest Cover has a different Crown Cover of >= 20 % but < 70 %, only 1 area would be classified differently:

These examples show that the classification system is quite conservative in assigning areas to the class Forest, High Canopy Density. On the other hand, it is rather generous in assigning areas to the class Forest Mosaic, which contains areas that have major gaps and are only about half covered by actual forest. The most common forest types of South-East Asia, i.e. the Degraded Evergreen / Mixed Forests and all Deciduous Forests (including the Dry Dipterocarp Forests), were normally - that means if there were no major gaps - classified as Forest, Low – Medium Canopy Density.

These characteristics have to be kept in mind when reading and interpreting the FCMP forest and land cover statistics. Dense evergreen forests, as many people imagine when discussing about forests in South-East Asia, can only be found in the class Forest, High Canopy Density. All other classes are more or less open or disturbed.

So far, only the classification of areas as Forest (of different canopy density classes) or Non-Forest by their quantitative characteristics has been discussed. The FCMP classification system also differentiates various Vegetation Types and Other Land Cover Types by their qualitative characteritics, like Evergreen Forest, Deciduous Forest, Wood / Shrubland, Bamboo, Agriculture, or Urban Areas. These types can be identified from Landsat TM satellite images by an experienced interpreter, mainly by their color.

Details on how to identify Vegetation Types and Other Land Cover Types will not be discussed here. The interested reader might instead refer to the FCMP Technical Notes 2, Interpretation and Delineation from Satellite Images.

The FCMP Forest and Land Cover Classification System can finally be summarized as follows:

Comparison of NFI and FCMP Results

Since all data on which the Forest Figures are based are available as digital maps, they can easily be overlaid. The overlay shows the following problem:

The differences in the Unclear Area have the following reasons:

• Subjectivity of Interpretation: areas with relatively few trees can sometimes be mapped as either Forest or Non-Forest, depending on the subjectivity of the interpreter.
• Effects of Time: the FCMP Forest Figure was produced 3 years later than the NFI Forest Figure. In some cases Forest may have been destroyed in the meantime.
• Differences in the Classification Systems: these are not likely to account for the major differences in the Forest Figures. Both the NFI and FCMP surveys had a minimum mapping area of 0.5 km². Both used the threshold of 20 % Crown Cover of trees of about 10 meters height for the definition of forest.
• Differences in Source Data Scales: FCMP used images at 1:250,000 scale, whereas NFI used images at 1:100,000 or 1:50,000 scale respectively. It is therefore theoretically possible that small patches of forest were mapped in the NFI survey, which remained undetected by FCMP.

A Sample Survey was conducted in the Unclear Area to help eliminate the differences between the NFI and FCMP Forest Figures and to obtain a Corrected Forest Figure. Key parameters guiding this survey are as follow:

• In the Unclear Area more than 300 Sample Areas were established. These Sample Areas were re-checked more carefully from Landsat TM digital satellite images taken in 1992/93 and in case of doubt from the aerial photos taken by FCMP in 1993/94.
• The Sample Survey covered only the Northern Part of Lao PDR (roughly 53 % of the country) due to time restrictions.
• The Sample Survey was conducted jointly by NFI and former FCMP national counterpart staff.
• The Procedure involved mixed team of NFI and former FCMP national counterpart staff who examined each Sample Area and classified it as either Forest or Non-Forest.

Post-Project Support through SMRP: Planning for the Future

The German Ministry of Economic Cooperation and Development and the MRC have agreed to fund two more years of FCMP post-project support (1999-2000) through the Sustainable Management of Resources in the Lower Mekong Basin Project (SMRP).

The SMRP aims to support the MRC, its member states and relevant partners in the region to develop, promote and implement strategies in Participatory Natural Resource Management (PNRM). To achieve this, the project focuses on the following key areas:

1. Description and analysis of framework conditions for the uplands of the LMB.
2. Facilitation, moderation and support for regular exchange of experience in PNRM of uplands with professionals and decision makers.
3. Establishment of pilot sites to test promising approaches in PNRM of uplands and validation by sector stakeholders.
4. Establishment of regional and national partnerships to design and jointly manage an Internet-based Information System on Natural Resource Management.
5. Elaboration of preconditions for a continuous consolidation and further improvement of the existing database regarding Forest Cover of the LMB and provision of demand-oriented monitoring and information products.

The FCMP post-project support through SMRP is provided in order to:

• Facilitate the distribution and marketing of the FCMP results to a wider range of users.
• Promote the utilization of the FCMP results in a wider range of applications.
• Further develop the built up technical skills and experiences.
• Introduce new technological concepts and techniques.

DISTRIBUTION AND UTILIZATION OF RESULTS

The technical result of FCMP is the GIS-based Natural Resources Information System containing

• Forest and Land Cover GIS data sets at 1:250,000 scale
• Forest and Land Cover Maps at 1:250,000 scale and 1:1,000,000 scale
• Forest and Land Cover Statistics

In addition, a comprehensive GIS data base containing additional information like topographical base data (hydrography, elevation) and infrastructural data (e.g. roads, populated places) has been built up.

Potential Users and Clients

The intention of the member countries of the MRC, the MRC itself, and the donors at the beginning of the FCMP was to make the FCMP results available to and usable for as many potential users and clients as possible.

The use of the FCMP results presently is limited by their scale of 1:250,000. As already pointed out above, the FCMP results were intended to be used at the macro-planning level, that is at the regional, national, and at most, provincial level. For applications below the provincial level one would need larger scales than 1:250,000.

Nevertheless, there is still a broad range of potential users involved in macro-level planning activities. The main group of potential users are, of course, the planning agencies of the FCMP countries, especially their environmental and forestry departments, as well as the MRC itself. In addition, there are numerous other potential users, e.g. private companies and donor funded projects working in the fields of forestry, natural resources management, hydroelectricity, and food security, to name only a few.

This chapter describes some potential applications of FCMP results in forestry and environmental macro-planning. It should be kept in mind that identification of areas as discussed below will not produce information sufficiently accurate to implement field activities, but rather give an indication of which areas should be considered for detailed data collection and planning and which areas do not need to be investigated any further.

As FCMP’s sister project, the regional Watershed Classification Project (WSCP) was established in 1997. It has generated comprehensive data on Slope and Watershed Classes. However, these data are only of limited use in the macro-level planning process, since they describe a desirable, but not necessarily real situation: e.g. Watershed Class 1 (on steep slopes) should be under permanent forest.

The combination of these WSC data with the FCMP data on Forest and Land Cover would take forestry and environment-related macro-level planning activities a significant step further: it would enable planners to pre-assess the impact of projects and measures on watershed quality and function and thus to optimize the allocation of resources and funds.

A prominent example are reforestation activities: by combining the WSCP Watershed Classes and the FCMP Forest and Land Cover Data one could identify areas that should be under permanent forest but are currently not forested. These areas would be priority intervention areas, where reforestation measures besides their immediate economic benefits would have the most beneficial effect on watershed quality and function.

A second example is the proper establishment of protected area networks: in this case the overlay of FCMP and WSCP data would help to identify forested areas on steep slopes, that means areas which generally need to be protected. Combining this knowledge with information on population density and pressure, one could then rank these areas by their potential endangering and thus identify areas which are in immediate need of protection. This would help to focus the generally limited resources available for protection (monetary as well as human) in a most meaningful way.

The criteria used to identify areas suitable for the establishment of fully commercially operating forest plantations are certainly different from the criteria used to identify areas for reforestation activities under watershed protection aspects. Present Land Use and accessibility play major roles in the identification of potential plantation areas. In many cases, the present Land Cover gives a quite strong indication of the present Land Use, especially in Non-Forest areas. Combining the FCMP Forest and Land Cover data with data on road infrastructure and population distribution might therefore help to identify areas where the establishment of forest plantations would cause minimal land use conflicts and at the same time can be expected to generate sufficient economic benefits.

Further Development of Technical Skills and Experience.

The key non-technical FCMP result is the human resource capacity building. In all the 4 member countries of the MRC, especially in Lao PDR and Cambodia, technical skills and experience in satellite image interpretation, GIS application, and database processing have been significantly strengthened during the operation of FCMP. These skills and the practical experience are of paramount importance for the further development of planning capacities in forestry departments. As such, the skills and experience gained by staff are probably even more important than the technical result.

Besides facilitating the distribution of FCMP results, SMRP aims at further development of these technical skills and experiences through the post-project support. To do so, SMRP provides the opportunity of continued on-the-job-training to the national counterpart staff through an international GIS and RS consultant.

On-the-job-training is considered central to improving the technical skills of national counterpart staff. Through the reworking of existing products (e.g. the 1:250,000 maps) and the introduction of new techniques (e.g. processing of digital satellite images) for the development of new products (maps at larger scale) the technical competencies of local staff will be broadened and strengthened.

With regard to analytical skills, these will be addressed by working with national counterpart staff in the field of GIS applications to extend the range of applications of FCMP results as discussed earlier.

Through continued training of the national counterpart staff, SMRP envisages to strengthen technical skills and experiences of the respective teams to even better address the requirements of the forestry departments of the partner countries for reliable data and knowledge generation and production of relevant and accurate products in the future.

SMRPs vision is to further develop the built up GIS and RS units to a point that they can independently design new products and react to and fulfill user requirements for existing and for new products in a flexible and market-oriented manner.

FUTURE TECHNOLOGY DEVELOPMENT

As discussed above, although digital satellite images are in principle preferable to printed ones for the generation of forest cover information, in its original project design, FCMP opted for printed satellite images because at the time local technical skills were still quite minimal and the costs of digital satellite images, software, etc. quite high. In the meantime, however, not only have national technical skills significantly improved, but costs (e.g., digital satellite images, hard- and software) have dropped considerably.

SMRP and its constituent partner countries have therefore agreed to take the logical next step and move from manual interpretation of printed satellite images to computerised processing of digital satellite images. A consultant has been employed to conduct training for former FCMP national counterpart staff on the processing of digital satellite images and to produce a series of prototype maps at larger scale together with the national counterpart staff. The first digital satellite images and the processing software necessary to launch this next phase have already been purchased by SMRP.

The most striking advantage of digital satellite images compared to printed satellite images is that GIS data sets and maps at larger scales can be produced in less time. SMRP intends to employ the digital technology to produce a series of GIS data sets and forest cover maps at scales between 1:50,000 and 1:100,000, in other words, maps that could be used for planning applications below the provincial level. Since the original FCMP products could only be used for planning at the macro level (i.e., regional, national, and only occasionally the provincial level) this would represent a major advance for the project as FCMP products reach a new, critical rung on the planning ladder, providing provincial planners with the accurate, up-to-date information that is essential to informed decision-making.