INTERNATIONAL SYMPOSIUM TOWARDS SUSTAINABLE LIVELIHOODS AND ECOSYSTEMS IN MOUNTAINOUS REGIONS 7-9 March 2006, Chiang Mai, Thailand

Shifting cultivation in Oudomxay and Luang Prabang, Lao PDR: A spatial and temporal assessment of the forest-farm-fallow

landscape mosaic using remote sensing and GIS Jay H. Samek

Global Observatory for Ecosystem Services, Department of Forestry, Michigan State University, 1405 S. Harrison Rd., Suite 101, East Lansing, MI, USA, 48823, Email:

samekjay@msu.edu

Abstract Laos is a mountainous country dominated by an agrarian population many who are practicing shifting cultivation in the uplands. Shifting cultivators are often identified as the culprits responsible for deforestation. However, there is relatively little empirical evidence showing that shifting cultivators cause deforestation. This study provides an empirical assessment, using remotely sensed earth observation data and geographic information systems (GIS), of shifting cultivation in two study sites as a methodological example with important possibilities to inform policy-making regarding the relative role of shifting cultivation on forest areas in Laos.

The analysis of multi-temporal Landsat ETM+ data for two areas north and northwest of the provincial capital Luang Prabang shows that more than 60 % of each area is in a persistent state of vegetation from 1986 - 2002. The analysis classifies high-resolution, remotely sensed, earth observation satellite data into vegetation and non-vegetation for nine images: two acquired March 1986, March 1992, and seven acquired between November 1999 and November 2002. Four of the images are from leaf-on periods and five are from leaf-off periods, which coincide to a fare degree with fallow or planted fields (leaf-on, wet-season) and bare fields (leaf-off, dry-season). Analysis of the inter-seasonal and inter-annual transitions (1999 – 2002) shows that only about a quarter of the two study site areas are in dynamic transitions between vegetation and non-vegetation land covers.

Keywords: Lao PDR (Laos), shifting cultivation, deforestation, remote sensing, GIS.

1 Introduction Laos is a rugged country with approximately 80 % of its 23,080,000 ha hilly or mountainous. Laos is dominated by an agrarian population with nearly 80 % of the population living in rural areas (UNESCAP 2005). Many of these people are practicing shifting cultivation in the uplands. The Lao Ministry of Agriculture and Forestry (MAF 1999) estimates that 35 % of the country’s population is practicing shifting cultivation or ‘slash and burn’ agriculture (as reported by Ducourtieux 2004).

The forest cover of Laos, like nearly all tropical countries, has declined in the last century. While definitions of “forest” are varied and estimates of current forest cover in Laos range from 54.4 % of the total land area (FAO 2000) to 40.0 % (Bunga 2002), nearly all agree that forest cover has declined from about 70 % of the total area in 1940. Identified causes for the forest cover decline range from illegal logging concessions, international demand for high-quality timber products, and population growth to name a few.

Not surprisingly, shifting cultivators in Laos are also often identified as being responsible, or at least partially responsible, for deforestation (ADB 1999, Foppes and Ketphanh 2000, UNEP 2002). The argument that high annual population growth in Laos (2.8%), together with the practice of shifting cultivation is driving deforestation appears logical and rational. And, while there is little doubt that forest cover has declined in Laos since the turn of the 19th Century (as it has over most of Indochina and the tropics as a whole), there is relatively little empirical evidence supporting the belief that shifting cultivators cause deforestation. Unfortunately, the population-shifting cultivator argument has found its way into policy-making at the national level in Laos, beginning with the 1996 Land Law, the basis for the national Land and Forest Allocation Programme (LFAP) aimed at stabilizing shifting cultivation practices. This study provides an empirical assessment of shifting cultivationand serves as a methodological example with important possibilities to inform policy-making regarding the relative role of shifting cultivation on forest areas in Laos.

Technological advances in geographic information systems and the acquisition of high-resolution remote sensing, earth observation satellite data provide the tools and means with which to analyze landscape level changes in land cover over time and over large geographic areas (Skole and Tucker 1993). Remote sensing and GIS analysis provide empirical data to help us understand more accurately the relationship between shifting cultivators and forest cover and the dynamic forest-farm-fallow mosaic. This study presents the results of an analysis of inter-annual and inter-seasonal (dry and wet season) remote sensing satellite data for two areas in Oudomxay and Luang Prabang Provinces, Lao PDR. The study uses Landsat Multi-Spectral Scanner (MSS) data acquired in 1986, Landsat Thematic Mapper (TM) data acquired in 1992, and Landsat Enhanced Thematic Mapper Plus (ETM+) data acquired between 1999 and 2002 to map the geo-spatial extents and transitions between areas of non-vegetation and areas of vegetation. The application of remotely sensed data and GIS analysis provides a method for assessing and modeling the complex spatial and temporal dynamics in these upland systems. Mapping large areas through such methods can provide important information regarding fallow-rotation periods as well as differentiate between pioneer shifting cultivation and long- and short-term fallow shifting cultivation, thereby providing empirical data regarding the relative role of shifting cultivation in deforestation.

2 Study Sites Two study sites were selected for the analysis (figure 1). Study site 1 covers a little more than 75,000 ha and is located about 60 kilometers north of the provincial capital, Luang Prabang. The site is a rectangular area located at 20°31´15.37 N latitude, 102°01´24.18 E longitude (upper left coordinate) and 20°18´14.55 N latitude, 102°19´12.02 E longitude (lower right coordinate). This area falls within three districts and two provinces: Nambak district, Luang Prabang province and Nga and Xay districts in Oudomxay province.

The second study area covers a little more than 73,600 ha and is located about 100 kilometers northwest of the provincial capital, Luang Prabang. The site is also a rectangular area and is located at 20°39´21.87 N latitude, 102°41´30.78 E longitude (upper left coordinate) and 20°26´22.79 N latitude, 102°59´01.07 E longitude (lower right coordinate). This area falls within the districts of Ngoi and Viengkham in Luang Prabang province.

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Table 1: Percent cloud cover per scene per site

% Cloud Cover Scene Date Study site 1 Study Site 2 Mar-86 0.00% 0.00% Feb-92 0.00% 0.00% Nov-99 0.00% 0.41% Mar-00 0.00% 0.00% Nov-00 0.18% 0.00% Feb-01 0.00% 0.00% Nov-01 0.37% 2.37% Feb-02 0.00% 0.00% Nov-02 0.00% 0.00%

Figure 1: Location of two study sites overlay with Landsat ETM+ scene

Both of the study sites cover mountainous areas and were selected based on a series of criteria. Because cloud cover can be problematic when using optical remote sensing data, a number of areas were eliminated simply due to a high percentage of cloud cover in one or more of the Landsat images used in the analysis. These two study sites were relatively cloud free in all nine images (table 1). In study site 1 only two scenes had clouds, though in each case the area affected was less than 0.5 % of the total study site area. In study site two there were also two scenes with clouds. In the November 1999 scene the area affected was small, 0.41 % of the total scene. The November 2001 scene was most impacted, with 2.37 % of the total area covered. In this scene, large high clouds cast shadows that were included in the cloud cover percentage. This was not a factor in the other scenes with clouds.

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Two ancillary data sets, the Food and Agriculture Organization of the United Nations (FAO) Forest Resource Assessment (FRA) 2000 data set (FAO 2001) and the European Commission Joint Research Centre (EC-JRC), Global Vegetation Monitoring (GVM) Land Cover Map 2000 data set (Stibig et al. 2000) were used to assist in selecting sites as well. The two study sites are located in areas assessed by the FAO FRA 2000 that includes closed forest, open and fragmented forest, other woodland, and other land cover (table 2). “Open and fragmented forest” account for 52.40 % and 34.32 % in the two study sites respectively. “Open and fragmented forest” is defined by FAO as:

“Land covered by trees with a canopy cover between 10 and 40 percent and height exceeding 5 m (open forest), or mosaics of forest and non-forest land (fragmented forest). Includes natural forests and forest plantations” (FAO 2001).

Table 2: FAO FRA 2000 data set – Forest cover for two study site

% Area by Cover Type Cover Type

Study site 1 Study site 2 Closed forest 37.25% 54.89% Open and fragmented forest 52.40% 34.32% Other woodland 0.13% 0.00% Other land cover 10.23% 10.78%

This cover type would, presumably, include shifting cultivation areas.

The two sites are located in areas assessed by the JRC-GVM analysis that includes explicitly shifting cultivation [Mosaic of Cropland / Other natural vegetation (Shifting cultivation in mountains)] among eight other classes (figure 2 and table 3). The two study sites include 38.84 % and 39.45 % “shifting cultivation” of the total areas respectively.

Table 3: JRC GVM data set – Land cover for two study site

% Area by Cover Type Cover Type

Study site 1 Study site 2 Tree cover, broadleaved, evergreen, closed and closed to open 1.13% 1.05% Mosaic: Tree cover / Other nat. vegetation or Cropland (incl.very degraded and open tree cover) 10.34% 6.03% Mosaics & Shrub Cover, shrub component dominant, mainly deciduous 47.41% 52.16% Mosaics & Shrub Cover, shrub component dominant, mainly evergreen 1.51% 1.05% Mosaic of Cropland / Other natural vegetation (Shifting cultivation in mountains) 38.84% 39.45% Cultivated and managed, irrigated (flooded, rice, shrimp farms) 0.76% 0.26%

Both of these data sets are derived from coarse resolution optical satellite data (SPOT Vegetation for JRC-GVM and AVHRR for FAO FRA 2000), but they provide some guidance as to where and how recent mapping efforts have identified vegetation and land cover in these complex upland areas. These data sets also reflect the limitations of coarse resolution data in assessing the

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landscape mosaic of shifting cultivation which operates at finer scales not captured by these coarse resolution sensors.

3 Methods

3.1 Data set descriptions The analysis uses multi-temporal optical remotely sensed Earth observation data acquired by the Landsat satellite series. Nine Landsat scenes were used in the analysis: one Multi-Spectral Scanner (MSS) data set acquired in 1986, one Thematic Mapper (TM) data set acquired in 1992, and seven Enhanced Thematic Mapper Plus (ETM+) data sets acquired between 1999 and 2002 (table 4). The Landsat TM and ETM+ scenes all have a spatial resolution of 30m2. The Landsat MSS scene has a spatial resolution of 57m2. The Landsat MSS sensor captures data in four spectral bands with the following wavelength ranges: band 1 0.5-0.6µm, band 2 0.6-0.7µm, band 3 0.7-0.8µm, and band 4 0.8-1.1µm, which correspond to green, red, and two near-infra-red spectral locations. The Landsat TM and ETM+ sensors capture data in six multi-spectral bands, a thermal band, and in the case of ETM+ a 15 m2 resolution panchromatic band. Band 1-5 and 7 capture data in the following wavelength ranges: band 1 0.45-0.52µm, band 2 0.52-0.60µm, band 3 0.63-0.69µm, band 4 0.76-0.90µm, band 5 1.55-1.75µm, and band 7 2.08-2.35µm (band 6 is the thermal band). These bands (1-5 and 7) correspond to blue-green, green, red, near-infrared, and two mid-infrared spectral locations.

Figure 2: Location of study sites with the JRC GVM Land Cover 2000

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Table 4: Landsat data used in analysis

Sensor Acquisition ResolutionMSS 17-Mar-1986 57m2

TM 6-Feb-1992 30m2

ETM+ 16-Nov-1999 30m2

ETM+ 7-Mar-2000 30m2

ETM+ 2-Nov-2000 30m2

ETM+ 6-Feb-2001 30m2

ETM+ 21-Nov-2001 30m2

ETM+ 9-Feb-2002 30m2

ETM+ 8-Nov-2002 30m2

The MSS and ETM+ data were processed to level 1G products (geometric and radiometric corrections applied) by EROS Data Center/United States Geological Survey (EDC/USGS). The TM scene was processed to level 1G by the National Resource Council of Thailand (the former ground receiving station for Landsat data in Bangkok, Thailand).

3.2 Analysis method The analysis method included a series of pre-processing steps prior to clustering and classification. The MSS and TM data sets were co-registered to the 1999 ETM+ scene through scene-to-scene identification of four registration points and using a polynomial transformation. The ETM+ data were all north-up, registered to UTM projection Zone 48 N, datum WGS84 using the definitive satellite ephemeris data. All data were co-registered to within 50m without further processing being required. Area-of-interest (AOI) files were created for the two study sites and these were used to subset each of the nine Landsat scenes. Normalized Difference Vegetation Index (NDVI) data sets were created for each of the scene study site subsets. NDVI is a common index derived through spectral ratios and is defined by the following equation:

NDVI = (Near IR band – Red band)/(Near IR + Red band)

For each study site scene a single file was created that included a stack of bands. The data stacks for the TM and ETM+ data were bands 3, 4, 5, and the NDVI. The stacks for the MSS data included bands 1 through 4 and the NDVI.

An unsupervised isodata clustering algorithm was used to group each of the study site data stacks into 35 bins. These clusters were then recoded to two classes: vegetation and non-vegetation (and cloud, cloud shadow in some scenes) using an overlay technique with a false-color composite (bands 4, 3, 2 in RGB) of the original data. Where clustering produced mixed classes, the thematic raster data were converted to ArcInfo GIS polygon covers and edited using the ArcEdit tools again with the false-color composite image in the background. A minimum mapping unit of 3 x 3 pixels (90m2) was applied, eliminating any small, isolated clusters below mapping standards. Spatial and temporal matrix data sets were created by combining the final thematic data sets for each of the time periods and study sites. Area statistics were calculated for each independent thematic land cover study site data set as well as for the matrix data sets that show transitions by land cover overtime.

All analyses were completed using ERDAS Imagine 8.7, ArcGIS 9.0, and Microsoft Excel.

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4 Results The image analysis results provide area estimates for vegetation and non-vegetation for each study site over nine dates that range from 1986 to 2002, providing a rough assessment of the relative land cover over time for these two upland areas. Table 5 shows the percentage of total area in vegetation and non-vegetation for both study sites and all nine data years. The range in percent forest cover for study site 1 is 90.15 % (Mar. 2000) to 97.64 % (Nov. 2001); and for study site 2 is 88.40 (Mar. 1986) to 97.07 (Nov. 2001). The range in non-vegetation for study site 1 is 2.36 % to 9.85% and in study site 2 2.93 % to 11.60 %. Based solely on these temporal data there is a dynamic landscape component in both of these study sites. Some of this can easily be explained by the seasonal effects on vegetation. The November scenes would likely have more apparent area in vegetation due to two factors: agriculture crops are growing in the fields and leaf-on phenology of deciduous floral species. The February scenes would likely show more non-vegetation as crops are harvested. The March scenes would show the highest areas of non-vegetation as all annual crops would be harvested, new fields or clearings would have begun, and leaf-off phenology would be most present. These data do show, however, that, at least in these two study sites, significantly large areas remain in vegetation from year to year.

Table 5: Percent vegetation & non-vegetation for each site and each

Study Site 1 Study Site 2 Scene Date Vegetation Non-Vegetation Vegetation Non-Vegetation Mar-86 94.21% 5.79% 88.40% 11.60% Feb-92 93.35% 6.65% 94.57% 5.43% Nov-99 96.11% 3.89% 95.15% 4.85% Mar-00 90.15% 9.85% 89.30% 10.70% Nov-00 97.33% 2.67% 95.38% 4.62% Feb-01 93.51% 6.49% 91.11% 8.89% Nov-01 97.64% 2.36% 97.07% 2.93% Feb-02 92.12% 7.88% 96.50% 3.50% Nov-02 96.31% 3.69% 94.82% 5.18%

Percent values for scenes that have cloud cover are calculated based only on the total area of vegetation and non-vegetation for that scene. Therefore, these values potentially maybe somewhat skewed, particularly with respect to the non-vegetation values simply as they account for a much smaller area than that covered by vegetation.

Given seasonal influences on the vegetation two simple assessments that look specifically at the persistence and the dynamics of vegetation cover can be made; one for the November (rainy-season influence) period which includes data from 1999, 2000, 2001, and 2002, and another for the February-March (dryer seasonal influence) period which includes data from 1986, 1992, 2000, 2001, and 2002. Tables 6 and 7 show the spatial-temporal persistence and dynamics of vegetation and non-vegetation, for the dry season data and wet season data, respectively. As would be expected, the areas that remain in persistent vegetation are smaller in the dry season data set than in the wet season. The range, however, still indicates that a majority of the area in both study sites remains in vegetation over the period from 1986 – 2002. Furthermore, the multi-temporal analysis shows that very little of the area remains in non-vegetation over time. The sites are dominated by persistent vegetation and areas in dynamic vegetation/non-vegetation transitions upwards of 27 %.

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Table 6: Multi-temporal analysis of dry season (Feb and Mar) data for both study sites

Study Site 1 Study Site 2

Temporal Range Persistent Vegetation

Persistent Non-

vegetation Dynamic Persistent

Vegetation

Persistent Non-

vegetation Dynamic

1986 - 1992 88.16% 0.60% 11.24% 84.26% 1.24% 14.49% 1986-1992-2000 80.75% 0.18% 19.07% 76.83% 0.51% 22.66% 1986-1992-2000-2001 78.16% 0.09% 21.75% 73.64% 0.37% 26.00% 1986-1992-2000-2001-2002 73.96% 0.05% 25.99% 72.29% 0.20% 27.51%

Table 7: Multi-temporal analysis of wet season (Nov) data for both study sites

Study Site 1 Study Site 2

Temporal Range Persistent Vegetation

Persistent Non-

vegetationDynamic Persistent

Vegetation

Persistent Non-

vegetation Dynamic

1999-2000 93.84% 0.38% 5.77% 90.95% 0.42% 8.63% 1999-2000-2001 91.74% 0.05% 8.21% 88.36% 0.08% 11.56% 1999-2000-2001-2002 88.53% 0.03% 11.43% 83.91% 0.07% 16.02%

The two-date analyses 1986-1992 (dry season) and 1999-2000 (wet-season) show the smallest area of land cover dynamics; 11.24 %, 14.49 % (dry season) and 5.77 %, 8.63 % (wet season). These areas grow as additional data sets in the time series are added to the analysis. The five date dry season data show 25.99% and 27.51 % of the total areas in some dynamic transition within the five date period. The four date wet season data show 11.43 % and 16.02 % of the total areas in some dynamic transition. These trends indicate that a two-date analysis does not provide a complete assessment of areas under dynamic transitions. Multi-temporal data sets are required to understand better the spatial magnitude of the dynamic state of vegetation transitions in these upland areas.

More complex spatial and temporal results are also provided by the multi-temporal, season-specific change matrixes. Dynamic patterns of vegetation clearing and recovery are evident. These areas point to where shifting cultivation is occurring. Table 8 shows those transitions greater than 0.50 % of the total land area for study site 1 for the dry season scene. These ten transition states account for 22.28 % of the total area and 85.71 % of the total dynamic transitions area. The four transition states over the five-date transition that account for the greatest area of the dynamic states are: (1) vegetated areas cleared in 2000 that reverted to vegetation in 2001 and 2002 (4.87 %), (2) vegetated areas that were only cleared in 2002 (4.20 %), (3) area that were non-vegetated in 1984 but then became vegetated in 1992 and have remained so (3.94 %, and (4) areas that were vegetated in 1984, cleared in 1992 and reverted to vegetation in 2000, 2001 and 2002 (3.24 %). Dynamic transition data are reported in table 9 for study site 2 (dry season) and study sites 1 and 2 (wet season). The dominant transitions states are similar in each dry and wet season data sets. These transitions account for 87.30 % (study site 2 - dry), 92.60 % (study site 1 - wet), and 93.61 % (study site 2 - wet) of the total transition areas in each study site and seasonal data set.

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Table 8: Dynamic transitions for study site 1, dry

season (1985 – 1992 – 2000 – 2001- 2002)

Transition Percent of Total Area v-v-nv-v-v 4.87% v-v-v-v-nv 4.20% nv-v-v-v-v 3.94% v-nv-v-v-v 3.34% v-v-v-nv-v 1.94% v-v-nv-nv-v 1.52% v-nv-nv-v-v 0.71% v-v-v-nv-nv 0.66% v-nv-v-v-nv 0.56% v-v-nv-v-nv 0.55%

22.28 % of the total area. 85.71 % of total dynamic area v = vegetation nv = non-vegetation

Table 9: Dynamic transitions for study site 2 (dry season: 1985 – 1992 –

2000 – 2001- 2002) and study sites 1 and 2 (wet season: 1999 – 2000 – 2001 – 2002)

Study Site 2 (dry) Study Site 1 (wet) Study Site 2 (wet) Transitions % of Area Transitions % of Area Transitions % of Area nv-v-v-v-v 7.79% nv-v-v-v 3.32% v-v-v-nv 4.33% v-v-nv-v-v 4.69% v-v-v-nv 3.21% nv-v-v-v 4.14% v-v-v-nv-v 2.99% v-nv-v-v 2.06% v-nv-v-v 3.78% v-nv-v-v-v 2.50% v-v-nv-v 2.00% v-v-nv-v 2.33%

v-v-nv-nv-v 2.27% v-v-v-v-nv 1.35%

nv-v-nv-v-v 0.69% nv-v-v-nv-v 0.61% nv-v-nv-nv-v 0.58% nv-nv-v-v-v 0.53%

v = vegetation nv = non-vegetation

Finally, the inter-seasonal dynamics of vegetation clearing and recovery reveal the degree to which shifting cultivation occurs in the area and its impact on persistent vegetation. This mapping exercise shows, at least for the period between 1999 and 2002, vegetation age and, therefore, fallow-length. The seasonal transition data for the two study sites, showing all transition state areas that are greater than 0.50 % of the total area, are reported in tables 10 and 11. Transition states shown for study site 1 (table 10) account for 94.96 of the total area and for study site 2 (table 11) 95.53 % of the total area.

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Table 10: Study site 1 inter-seasonal, inter-

annual transitions (1999 – 2002)

Transitions Percent of Total Area v-v-v-v-v-v-v 76.52% v-nv-v-v-v-v-v 4.33%

v-v-v-v-v-nv-v 3.01%

v-v-v-nv-v-v-v 1.89% nv-v-v-v-v-v-v 1.82% v-v-v-v-v-nv-nv 1.49%

v-v-v-v-v-v-nv 1.45% v-nv-v-nv-v-v-v 1.33% v-v-v-v-nv-v-v 1.07%

nv-nv-v-v-v-v-v 0.92% v-v-v-nv-v-nv-v 0.57% v-v-nv-v-v-v-v 0.56% 94.96 % of total area

Table 11: Study site 2 inter-seasonal, inter-

annual transitions (1999 – 2002)

Transitions Percent of Total Area v-v-v-v-v-v-v 73.52% v-nv-v-v-v-v-v 3.84% v-v-v-v-v-v-nv 3.59% v-v-v-nv-v-v-v 2.51% v-nv-v-nv-v-v-v 2.22% nv-v-v-v-v-v-v 2.02% v-v-nv-v-v-v-v 1.76% v-v-v-v-nv-v-v 1.71% nv-nv-v-v-v-v-v 1.63% v-v-v-v-v-nv-v 1.09% v-v-nv-nv-v-v-v 1.05% v-nv-nv-nv-v-v-v 0.60% 95.53 % of total area

The analysis shows that the three year period between 1999 and 2002 more than 70 % of the area in both study sites remained in persistent vegetation. The analysis also reveals that for all possible transition states in the seven date analysis transitions never accounted for more than 4.33 % of the total area. The landscape mosaic in each of the study sites is quite complex both spatially and temporally. This analysis indicates that only about a quarter of these areas (23.46 % - study site 1 and 26.44 % - study site 2) are under shifting cultivation. Figure 3 shows the spatial patterns for both study sites of where the dynamic land cover changes are occurring over the 1999 – 2002 period of analysis. These areas are dispersed throughout the two study sites. Green indicates persistent vegetation; black is where cloud and cloud shadows have been detected and excluded; brown are transition categories, each greater than 100 ha, that account for more than 90 % of the total area where dynamic changes occurred; and the light yellow are the remaining dynamic areas (all less than 100 ha transition categories).

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5 Conclusion The analysis of multi-temporal and inter-seasonal, remotely sensed, earth observation satellite imagery provides empirical data regarding the spatial and temporal dynamics and vegetation persistence in the uplands of Laos, where shifting cultivation is present. Mapping the dynamic vegetation/non-vegetation transitions for the complete nine scenes (1987 through 2002) for each study site shows that 69.65 % and 64.41 %, respectively, of the total areas analyzed remained in persistent vegetation. Without data for all years between 1986 – 1992 and 1992 – 1999 we cannot say for sure if some areas have been cleared or not. However, these data do provide evidence suggesting that shifting cultivation has some bounds and does not affect all primary forest areas.

Figure 3: Inter-seasonal, interannual land cover Nov. 1999 – Nov. 2002

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This analysis is a preliminary study used primarily to demonstrate and refine a method for using remote sensing and GIS processed to assess the dynamic forest-fallow-farm landscape in the uplands of Laos. Without related in situ data to validate and calibrate the remote sensing analysis results for vegetation and non-vegetation it is difficult to assign relative error or accuracy to the results. Certainly there is some error due to scene-to-scene co-registration inaccuracies and to the phenology of the leaf-off period of some of the data. These types of error would contribute to the vegetation transitions in which these areas would be over-estimated. The areas of persistent vegetation in this analysis would therefore be underestimates.

Persistent vegetation does not translate directly to primary or ‘pristine’ forest specifically or even forest cover generally. It does, however, serve as a proxy for forest. Given current climatic and geographic factors and an absence of human disturbance these areas would be forested. It is reasonable to expect the area in persistent vegetation from 1986 – 2002 (69.65 % and 64.41 % for the two study sites) is a mosaic of primary forest, long term fallow, and possibly agriculture crops, and that this mosaic is dominated by forest cover.

This type of assessment would benefit significantly from additional annual and seasonal data. Unfortunately, while the historical Landsat data does go back to the early 1970s, data availability with annual or seasonal temporal frequency dating back to 1972 is non-existent. Fortunately, more and more earth observation satellites are being launched, some carrying optical sensors with remarkably very high spatial resolutions (IKONOS, QuickBird, SPOT 5 to name a few). The future for accurate measurement and monitoring of land use and land cover change throughout the uplands of Laos, Southeast Asia and the world is very promising. These analyses will provide important empirical data useful to policy makers whose decisions significantly affect land users in areas such as Luang Prabang and Oudomxay provinces in Laos.

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