Charles M. Schweik, Harini Nagendra and Deb Ranjan Sinha ...ifri/Publications/R031-26.pdf · are...

312 © Royal Swedish Academy of Sciences 2003 Ambio Vol. 32 No. 4, June 2003http://www.ambio.kva.se

Article

INTRODUCTIONThe impact of humans on the natural environment has increaseddramatically over the last quarter of a century (1). Problems suchas the loss of biological diversity (2, 3), watershed degradation(4), and the build-up of greenhouse gases are prominent exam-ples. There is a sense of urgency to identify innovative forestmanagement institutions (economic, political or communal) thathelp curb these problems.

There is a sizable and growing body of literature that seeksto describe and outline lessons learned from successful commonproperty regimes (5–12). In her seminal book Governing theCommons (7), Ostrom argues that local communities have thecapacity to create long-enduring systems for managing commonpool resources (CPR), such as fisheries, irrigation systems or for-ests. Ostrom contends that there are cases of effective environ-mental management institutional designs “out there” to be stud-ied. These institutions may be as diverse and varied as ”culture”appears to be.

However, institutions, like the natural resources they govern,are susceptible to change or destruction. Clearly, there is a wholesuite of pressures of change on locally established environmen-tal institutions. A substantial body of research already existswhich looks at the impacts of globalization on changes in theeconomic (13), sociocultural (14–17) and political (13, 18–20)systems of various countries. Increasing pressure by nongovern-mental organization donors could be considered another dimen-sion of globalization processes. “Intra-country” environmentalpolicy trends, such as those towards “nationalizing” forest re-sources or developing national level forest management frame-works in Nepal, India, and sub-Saharan Africa may result in top-down pressures that lead to changes in locally crafted, perhapslong-enduring, environmental institutions.

Among developing countries, Nepal has proved to be a leaderin experimenting with participatory systems of forest governance(21). However, while on paper there is still much enthusiasmfor encouraging participation, the devolution of state responsi-

bilities does not quite match the rhetoric in practice. For exam-ple, both Gilmour (22) and Messerschmidt (23) have documentedcases in Nepal where community-based forest management fellinto disuse with the forest nationalization program in the 1960s.These researchers note that recent programs involving top-downapproaches have been largely unsuccessful in reviving these in-stitutions due to their sweeping generalizations about possibleneeds and benefits local communities may derive. Privatizationof traditionally communal land and some government-ownedland is another example of the extent of globalization processesin environmental management (24). Locally established institu-tions might be altered, even completely replaced, as a result ofthese pressures.

We suspect that these kinds of pressures may be leading tomajor changes in the composition and diversity of forest man-agement institutions in a similar fashion to what is documentedin the economic, political and cultural realm. Changes in eco-nomic, political and cultural attributes of communities are of-ten very apparent. Institutional change is more transparent butjust as real. We therefore argue here that there is an urgent needto inventory innovative institutions that have effectively man-aged forest resources. This need becomes especially importantin view of the concerns raised by Messerschmidt (23) who re-minds us of the importance of maintaining and preserving abroad range of regional sociocultural variety. This diversity couldact as a cushion by providing a large selection of options andsolutions at times of crises and conditions that we are yet to en-counter. But a significant problem exists: how do we find in-stances where these institutional innovations exist? How do welocate cases of forest management worthy of detailed (and ex-pensive) case study?

Traditionally, cases for field research are often selected forpractical reasons: i) either the researcher has intimate knowledgeof the specific case already; and/or ii) the case is geographicallyproximate to where the researcher works; or iii) somehow theresearcher has learned that there is something interesting at aparticular locality that needs further study. While these ap-proaches can be effective, they may not always identify inter-esting (or unknown) cases in the region containing innovativeinstitutional designs. In some cases, expensive fieldwork mightyield little in terms of new knowledge and understanding of howhumans can effectively manage forests. The chosen cases maysimply be examples of open-access situations. And through thesetraditional ways of locating study sites, some—possibly many—innovative locally established, often culturally specific institu-tional designs may go undocumented simply because no re-searcher or policy analyst knows that they exist. This is prob-ably most true in developing countries where documentation ofapproaches to environmental management may be lacking or notshared beyond the locals in the case itself. Given the urgencyof the environmental problems described above, we argue thatthere is a critical need to develop mechanisms that, cheaply andeffectively, help to identify where effective local environmen-tal institutions might exist and help target where more detailed(and often expensive) field study should be conducted. In short,we face a “needle in a haystack” problem. We need more effec-tive ways of locating these institutional “needles.”

Charles M. Schweik, Harini Nagendra and Deb Ranjan Sinha

There is a critical need to locate innovative forest man-agement institutions that significantly impact forest coverchange. This research presents an initial ”proof of concept”methodology which combines deforestation theory withsatellite image change analysis to identify forested areasthat, theoretically, should probably not be there. Ten such“forest anomalies” are identified using temporal analysisof Landsat TM imagery of the Chitwan district in Nepal,linked with a GIS database on roads and a visual est-imation of topography. A rapid field reconnaissance isundertaken to determine which of these anomalies exhibitinteresting forest management innovations. Based on thisinformation, one case is selected for detailed field study:this turns out to be a major case of community forestry anda premier ecotourism initiative that we were not aware ofuntil we undertook this analysis. The utility and limitationsof the method are described for monitoring trends in forestcover change.

Using Satellite Imagery to Locate InnovativeForest Management Practices in Nepal

313Ambio Vol. 32 No. 4, June 2003 © Royal Swedish Academy of Sciences 2003http://www.ambio.kva.se

This paper presents our initial methodology for locating in-novative forest management practices. We combine fundamen-tal theory on deforestation processes in the region of Nepal witha simple Geographic Information System (GIS) and Landsat sat-ellite image-based change analysis to identify forested areas that,theoretically, should probably not be there. Throughout this pa-per, we refer to forests as areas with broad vegetation or canopycover: these include tree plantations, and primary and second-ary forests. We refer to these locations as ”forest anomalies.”Our study area, the Chitwan district of the Nepal Terai, has ex-perienced rather high levels of deforestation over the past fourdecades, owing largely to government sponsored land settlementand human migration to the region. Increased government aware-

sion on the utility of the methods described for selecting andstudying interesting cases of land use/land cover change.

STUDY AREAWe selected the eastern side of the Chitwan District in southernNepal (Figs 1 and 2) as the location for our study. This districtis located roughly 100 km southwest of the capital city ofKathmandu and lies in the plain area of the country called “theTerai.”

In the northern part of the district there are the foothills ofthe Himalayas, in the south, relatively flat terrain. As late as the1950s, this region was covered with tropical moist deciduous

Figure 1. Map of Nepal. The study area is theeastern half of the Chitwan district.

Figure 2. Landsat TM color composite of the East Chitwan, Nepal. 1/24/1989.(Red-band 4, Green-band 2, Blue-band 1). Red areas indicate vegetation (e.g. forest canopy, agriculture).

ness and support of community-based institutions ofmanagement, has encouraged some recent reforesta-tion. The area is thus a dynamic mosaic of shifting for-est boundaries, providing a particularly challengingcase study. Areas of particularly rapid forest changeare identified using Landsat TM imagery of two dates,and linked with a GIS database on roads and topogra-phy. The locations of these anomalies are documentedin a GIS for further study. A rapid field reconnaissanceis then undertaken to determine which of these forestanomalies might exhibit some interesting forest man-agement information. Based on this information, onecase is selected for detailed study. The paper describeswhat we find in this case and concludes with a discus-

Chitwan District

TikauliForest

East-WestHighway

BarandabarForest

RaptiRiver

Northern section of theRoyal Chitwan NationalPark (RCNP)

Himalayanfoothills


forests, with some interspersed marshy grasslands. For instance,substantial grassland exists in what is now the Royal ChitwanNational Park (RCNP). The dominant forests are dry-to-moder-ately wet Shorea robusta (Sal) with intermixed stands of Pinusroxburghii found in higher altitudes. Dalbergia sisso (Sissoo) andSaccharum-Phragmites-Themeda plant communities can befound in flood basins along the rivers (25). Until the 1950s, thisregion was infested with malaria-carrying mosquitoes, keepingthe population low (26). The indigenous inhabitants of the re-gion are called the Tharus and are a mobile population who areknown to have practiced a form of shifting cultivation in theplains of the Terai in the past (26, 27).

In 1953, a major effort to eradicate malaria began in 1953 bythe World Health Organization (WHO) and the United StatesAgency for International Development (USAID), and by 1970,91% of the previously affected region was declared malaria-free(25). These and other factors such as political changes (e.g. theoverthrow of the Rana regime in 1951), changes in legislationrelated to forest management and planned land colonization or-ganized by the Rapti Valley Development Programme and Ne-pal Resettlement Company led to a rapid settlement of the re-gion (27). Migrants settled in remote forested areas and con-verted much of these lands to agriculture. From 1927 to 1977,it is estimated that the forested areas in the Terai decreased byalmost 60% (28). By 1979 a major all-weather road linkedNarayanghat, the largest town in the district, to Kathmandu. Dueto all the above processes, Chitwan has experienced a doublingof population between 1971 and 1991 with an annual popula-tion growth rate of 3.3% (26).

Today, east Chitwan is one of the most productive and im-portant agricultural regions of the country. The evidence of ma-jor international NGO and government-sponsored irrigationprojects are easily seen from a drive along the east-west high-way. Many of the current inhabitants are farmers heavily depend-ent on forest resources for animal fodder, fuelwood, and timber(29, 30). A sizable portion of the available flat terrain in the re-gion is now cleared for agriculture or other development (e.g.settlements) with the exception of the RCNP land in the south,the Barandabar Forest to the west and forested regions of theHimalayan foothills to the north and northeast (Fig 2).

METHODOLOGYCentral to this study is identifying locations of forest anomaliesacross a landscape, in a cost-efficient manner. The approach weare advocating involves 6 sequential steps: i) gathering of sec-ondary data; ii) consideration of theoretical drivers of deforesta-tion in the landscape being studied; iii) consideration of appro-priate spatial and temporal scale for studying forest cover change;iv) satellite image sampling and processing; v) initial identifica-tion of forest anomalies; vi) rapid field reconnaissance; and vii)detailed field study of the selected case.

Gathering of Secondary Data

The project was initiated by gathering several sources of sec-ondary data. We acquired 1:25 000 scale topographic maps forthe region from the Topographic Branch of the Department ofSurvey in Kathmandu in earlier research trips in 1994 and 1996.A few additional maps were acquired during our visit in 2000for Step vi), rapid field reconnaissance. Aerial photographs ofthe region were also reviewed back in 1994 and 1996 for ear-lier research on landcover change analysis of a watershed in theregion (30). These data were helpful in the ’Theoretical Driv-ers’ and ’Satellite Image Processing’ steps outlined below.

Theoretical Drivers of Deforestation in East Chitwan

The first step toward identifying forest anomalies is a theoreti-cal consideration of important drivers of forest-cover change (and

land-cover change in general) in the context of the geographicregion of interest. Prior research related to human dimensionsof environmental change in Chitwan ( 25–27, 29–31, the broaderTerai (33), Nepal in general (28, 34–37), and broader meta-analysis (32, 38) helped to identify an initial list of proximatedrivers of deforestation (32) in East Chitwan. This analysis isalso based on our own experience in the region (nearly a yearin total between the authors), and from discussions with col-leagues and villagers who live there.

Since the 1950s, infrastructure extension and agricultural ex-pansion has been the primary cause of deforestation in Chitwan.Mobility was enhanced by a new district-wide road infrastruc-ture; the Mahendra highway running in the east-west direction(Fig 2), and a northern connection to Kathmandu (26). Urbanand rural settlements expanded from 1950 to the current day.Early (1950s, 1960s) aerial photographs of the region revealdense Shorea robusta forest throughout the valley—so dense thatit is difficult to recognize familiar topographic patterns in thelower hills to the north (30). A cursory analysis of the full setof photos from the 1950s through the late 1980s revealed a pat-tern of settlement and agricultural expansion along the flat ar-eas of the region following road and river networks and then ex-panding out into the flat terrain of the valley. Large agency- orNGO-funded irrigation projects is another component of infra-structure extension that encouraged conversion of forests to ag-riculture, driven by national policies to enhance agriculture pro-duction in the region.

Wood and fodder extraction are also major proximate causesof deforestation in the region. Residents who rely on the Shorearobusta forests for thatch, timber, polewood, fodder, firewood,and livestock grazing regularly use the same road networks toaccess these forests (26).

The locations of new or expanded settlements have been de-termined largely by the pre-disposing factor of topography. Re-sults from the recent Chitwan Valley Family Study (39) reportthat many residents of Chitwan are almost entirely dependenton field cropping for their sustenance (26). Flat areas adjacentto rivers are, therefore, the preferred land for settlement and suchchoice areas were often the first forest to be converted to agri-culture.

Ideally, a process to identify forest anomalies would considerall proximate drivers of deforestation in the study context insome formal method. In situations where substantial geographicinformation system (GIS) data exist, sophisticated methods couldbe helpful in identifying anomaly locations using topographicalfeatures such as slope and aspect. In the context of developingcountry research (including ours), one does not have easy ac-cess to digital geographic data. Given the rapid elevation changein this area from the plains to lower hills of the Himalayas, it isnot a trivial task to digitize and create a model of topographicrelief. Therefore, for this study, anomalies are identified using3 primary variables driving where deforestation occurs in thisregion: i) transportation infrastructure (we developed a roadsgeographic information system layer); ii) topography, based ona simple visual estimation of topographic relief from the imageand from available topographic maps; and iii) a general under-standing of population growth from discussions with people liv-ing in the region and secondary sources (e.g. 26).

Consideration of Appropriate Spatial and Temporal Scale

The next step in identifying forest anomalies is to consider de-forestation in the context of the region and culture(s) being stud-ied and relate, conceptually, the actions of actors conducting de-forestation (or reforestation) to appropriate spatial and tempo-ral scales of data being used to study the phenomenon of inter-est. Evidence of deforestation will occur at multiple spatialscales. That is, the spatial grain of deforestation can be extremelyfine, as in the case of people removing specific plants of a par-


ticular valuable species, or it can be coarse as in the loss of en-tire stands of trees when land is converted to agriculture. Simi-lar scaling issues occur in terms of temporal grain. For exam-ple, the rate of regrowth of forest vegetation in tropical forestslike the Brazilian Amazon will be very fast compared to re-growth of deciduous forests in Indiana, USA. To capture defor-estation patterns in the Amazon may require several more timesteps of satellite images or aerial photos than might be neededto capture similar phenomenon in the Indiana case. The grainof the phenomenon of interest drives the choice of appropriatedata at comparable scales. In this pilot study, we focus on rela-tively coarse-grained deforestation activities—the presence orregrowth of forest stands.

Satellite Image Sampling and Processing

Having an idea of the phenomenon we wish to understand pro-vides the guidance needed to select appropriate satellite imagetechnology. There are several satellite image platforms available,with various capabilities (see e.g. 40).

We use US Landsat Thematic Mapper (TM) images in thisstudy. These images are useful for the “searching for forest man-agement anomalies” concept we introduce here for 5 reasons: i)Landsat images cover a broad spatial extent (approximately 185x 185 km); ii) the Landsat data archive covers most of the Earth’sterrestrial surface between 81° N and 81° S latitude and coversa relatively long temporal extent—1972 to present day; iii) thesesatellites return to the same location of the earth approximatelyevery 16 days, providing many images of any particular region;iv) there are 7 sensors that collectively provide enough infor-

mation to distinguish between various broad types of land coversuch as forest, agriculture, soil and water; and v) the spatial reso-lution is relatively fine, with a pixel size of 28.5 x 28.5 m,allowing us to detect the landcover change of interest here.

We selected 2 nearly cloud-free Landsat TM images: Janu-ary 24, 1989 and March 27, 2000 as we felt a two-image timeinterval over 10 years was adequate to detect de- or re-foresta-tion processes, yet broad enough to capture interesting patterns.Our intention was to select images at nearly the same date ofthe year, but unfortunately many of the images in the existinginventory had significant cloud cover. Given that Januarythrough March is the winter season in Nepal, we are not veryconcerned that the images were not taken in the same month ofthe year.

Once acquired, we georeferenced the 1989 image using1:25 000 scale topographic maps with a root mean square (RMS)error of less than 0.7. The 2000 image was georeferenced to the1989 image with an RMS error of less than 0.5 (i.e. one half ofa pixel). Given the complex topography in this region, whichvaries from the relatively flat inner Terai plains to the foothillsof the Himalayas, obtaining image-to-image georeferencing ac-curacies as high as this is a difficult, though important task.Visual verification of georeferencing accuracy was conductedby overlaying various image bands from the 2 images on oneother, using the software ERDAS Imagine™.

Initial Identification of Forest Anomalies

The 10 marked areas in Figure 3 capture the forest anomaly lo-cations identified using a comparison of the 1989 and 2000 TM

Figure 3. Landsat TM color composite of the East Chitwan, Nepal. 3/27/2000. (Red-band 4, Green-band 3, Blue-band 2). Redareas indicate vegetation (e.g. forests, agriculture canopies). 10 forest anomalies identified.

TikauliForest

BarandabarForest

Northern section of theRoyal Chitwan NationalPark

Himalayanfoothills

32

1

4

5

67

8

9

10


images, as well as knowledge of the theoretical proximate driv-ers of topographic relief (visually assessed) and transportationnetworks in the valley (we have a roads GIS layer not shown inthe Figures). From their spectra, these looked like areas of treecanopy that may or may not be considered “forest.” These veg-etated areas exist in flatter areas with relatively high humanpopulation and nearby transportation networks. The question iswhy, if they are indeed forest or tree stands, do they still exist?Why have they not been converted to agriculture or develop-ment?

Rapid Field Reconnaissance

With the anomaly areas identified, the next step in our processwas to visit these locations briefly, to get a quick sense as towhat they are and why they exist. Co-author Nagendra spent 4weeks visiting these locations in March of 2001 and holding briefdiscussions with local villagers to understand what was happen-ing there. In brief, she discovered the following:

Figure 3, Area 1: This site has an interesting inverted L shape.The region has evidently gone through an increase in tree coverbetween 1989–2000, although surrounded by clearings. The geo-metrical shape and rather clearly defined borders led us to as-sume this was a human-created patch of tree cover. The March2001 field visit identified this as a Sissoo plantation under thecontrol of the Nepal Forest Department.

Figure 3, Area 2: This circular remnant patch of trees is con-tiguous with the Sissoo plantation in Figure 3, Area 1 in the 1989image, but shows up as an isolated patch surrounded by culti-vation in the 2000 image. This patch, located within thePadampur Resettlement area, was gone in our 2001 field visit.This is the most dramatic case of recent and rapid deforestationthat we observed in the region.

Figure 3, Area 3: This patch of forest appears thick anddensely vegetated in the 2000 image, although located at the bor-der of village clearings. But it is to some degree a de facto openaccess forest. All the Sal trees in this patch had been selectively,and illegally harvested, since they fetch a high price in the tim-ber market. Thus, our methodology for identifying spatial ano-malies allowed us to locate a forest that is clearly at risk andbeing systematically harvested for timber. Limitations of imageresolution however meant that we observed the presence of adensely wooded forest, but could not perceive the changes in for-est composition.

Figure 3, Area 4: This area in 1989 was maize and rice fields,but about 9 years ago was converted to a private Sissoo planta-tion by 3 brothers whose goal is to eventually harvest it and sellthe timber. This is an example of private entrepreneurship thathad become popular in parts of the Terai during the past dec-ade: although a recent outbreak of Sissoo disease in the area hassomewhat dampened interest in this activity.

Figure 3, Area 5: This region was identified as another smallcircular patch of reforestation, and we found that it is now a pri-vate, well guarded plantation where Sissoo trees and Meliaazaderach (Bakaino, a native tree species with fuel, fodder andmedicinal uses) were planted.

Figure 3, Area 6: This patch of forest is located just outsidethe RCNP, along its northern edge. Two community forests man-age the forested area within this patch with the assistance of theRCNP, under the umbrella of the park’s Buffer Zone Develop-ment Programme (BZDP). When the forest was handed over tothe local communities for protection and management, in 1996,Sissoo, Bakaino and other tree species were planted in the for-est. Grazing and fuelwood extraction were also prohibited sincethis time, and the forest boundaries are fenced with barbed wireto prevent encroachment and grazing.

Figure 3, Area 7: This area also falls within the purview ofthe BZDP, and is dominated by a riverine forest with Sissoo,Bombax ceiba (Simal), Lagerstroemia paniculata, and other tree

species. In 1994–1995, with the assistance of the Forest Depart-ment, an area of 118 ha was placed under the protection of theKuchkuche Forest User Group (FUG). The entire region wasfenced, planting was undertaken and grazing and extraction wereprohibited.

Figure 3, Area 8: This area is a former National Forest areathat became a community forest in 1985, again as part of theBZDP. The area is managed by a FUG in consultation with theRCNP administration. In 1995, 100 ha of the forest was plantedwith Sissoo, Bakaino and Acacia catechu (Khair, another localtree species used for fuel and fodder), on the initiation of thelocal District Forest Office (DFO). The forest protection com-mittee earns an income of about 1 200 000 Nepalese Rupees (ap-proximately USD 16 216) per year from tourism and the sale ofnon-timber forest produce: a powerful incentive to maintain theforest cover.

Figure 3, Area 9: This patch of forest, called Icharni Island,is also contiguous with the RCNP, but under the protection ofthe RCNP administration. Much of this area was covered withtall elephant grass until 1993, when a major flood initiated achange in vegetation, leading to a riverine forest dominated bySissoo and Simal. It is protected since it falls within the park,and also naturally sheltered from human activity by the rivers.

Figure 3, Area 10: This area falls within the Baghmara Com-munity Forest (BCF), which borders the RCNP on its northernside. During our field visit, we found that this community for-est also falls within the purview of the BZDP. We learned inthe rapid assessment that this has received publicity as the pro-gram’s most dramatic success story in terms of reforestation,conflict resolution, and income generation for the local commu-nities. We did not know about this case previous to this Landsat-based analysis.

Detailed Case Study Methods

The rapid assessment helped us understand in other sites whythey exist (or did not exist in the case of Fig. 3, #2). Of theanomalies, the BCF site (Fig. 3, #10) appeared to be innovativeand interesting from an institutional perspective. This site waschosen for detailed study, with fieldwork conducted by coau-thor Sinha in the summer of 2001. Semistructured interviewswere scheduled with individuals perceived to have a direct stakeon the study areas. Background information was collected on theindividuals and the study areas from publications and other re-source persons before conducting the interviews. A printout ofa color composite image of the study area helped us hold dis-cussions with the respondents about the changes in land use/landcover between 1989 and 2000. Global Positioning System (GPS)readings were taken to confirm our positions.

FINDINGS: DETAILS ON THE BAGHMARACOMMUNITY FOREST ANOMALYThe BCF borders the RCNP on its northern side and falls withinthe Buffer Zone (BZ) demarcated by His Majesty’s Governmentof Nepal through a reform in the National Forest Policy in 1993.This policy reform allowed the National Parks in Nepal to cre-ate BZs around their boundaries for several reasons: i) to incor-porate more forested area towards sustaining increasing numbersof wildlife; ii) to prevent illegal natural resource extraction fromthe park by providing these resources from the forests in the BZ;and iii) to improve the socioeconomic conditions of people stay-ing within the BZ by providing them with various opportunitiesof livelihood using the resources available in the BZ forests.Later in 1996, an additional legislation enabled the NPs to share30–50% of its revenue with the BZ community.

Prior to 1989, the BCF was subject to substantial pressure forconversion to agricultural land by migrants into the district.While a property survey conducted by the Land Records Office


around 1973 (the same year RCNP was formed) enabled theDFO to assert its ownership over the land and discontinue agri-culture, illegal grazing, collection of fodder, firewood and tim-ber continued unabated. Concurrently, the community was alsodisallowed access to these resources from within the RCNP, thusincreasing the pressure on the present-day buffer areas. The feel-ing of alienation of ownership after the land survey and unavoid-able needs for everyday use led to the inevitable—indiscrimi-nate use of the forest resources without any attempt to use itsustainably. By 1989 (the year of our first satellite image), for-est vegetation was severely degraded.

In 1988, the King Mahendra Trust for Nature Conservation(KMTNC) realized that forests outside the RCNP need to be pro-tected and nurtured with the active participation and assistanceof the local community. With an initial grant of USD 10 000from USAID, a tree nursery and plantation was started on 1.02ha of land belonging to a local KMTNC ranger. By the next year,with the support of approximately 25% of the local populationand permission of the District Forest Officer, an area of 32 hawas fenced off by the KMTNC in the BCF area and a planta-tion of Sissoo and Bakaino was initiated. Two years of persist-ent negotiations and dialogue won over the majority of the popu-lation in favor of the plantations. Currently, the BCF has 401ha under a mosaic of plantations, natural regeneration andgrasslands.

While the first plantation was established in 1989, the forestwas handed over by the District Forest Officer to the commu-nity only in 1995. Until this time, the FUG that was formed tomanage and protect the forest was allowed to extract limitedamounts of firewood and fodder. Financial assistance by USAIDand Biodiversity Conservation Network also enabled this proc-ess. After 1995, the FUG came out with strategies to generateits own funds. Lopped timber from the plantations was sold andan eco-tourism project was initiated. Hotels proximate to theRCNP started bringing tourists on elephant safaris into the ad-joining forests, with entry fees going to the FUG. Additional ac-tivities such as nature bird walks and canoeing were also intro-duced later.

These activities have ensured a steady income for the FUG,which has been used for developmental activities. Substantialindirect benefits have also accrued from increased employmentin the tourist hotels. While the members are allowed to collectfodder any day of the year, firewood collection is restricted to 4days in a year. Activities like grazing, logging or hunting isstrictly forbidden inside the BCF and violators are fined and havetheir animals and implements confiscated. Since the initiationof BCF, KMTNC has initiated similar programs and at presentaround 6000 ha is under this form of community forestry in 10Village Development Committees in Chitwan (pers. comm. S.Choudhary).

In sum, the detailed field inventory of this forest anomaly re-vealed an extremely interesting case where reforestation occurreddue to substantial incentive structure shifts to local communitymembers through a recent ecotourism effort.

CONCLUSIONThe aim of this paper is to show a proof of concept where sim-ple change analysis and deforestation theory help target field re-search in an effort to streamline the search for innovative envi-ronmental management efforts. We argue that field research isoften targeted because of proximity to a research base or for otherpractical reasons. In this age of rapid deforestation, it is impor-tant to develop methods to identify and document innovative in-stitutional arrangements that somehow are effective in balanc-ing human pressures on forests in a particular region. The analy-sis we present here strongly supports the utility of our proposedmethod. It also highlights some issues and challenges.

The findings here are exciting, for it supports the idea that asimple Landsat image change analysis using color composites,one that required little image processing steps such as radiomet-ric calibration or atmospheric correction, coupled with a simpleconsideration of proximate drivers of deforestation in the region,and a “rapid field assessment” of initial forest anomalies, canlead to the discovery of interesting environmental institutionalinnovation. In this case, the technique led us to locate and in-ventory what turns out to be one of the premier ecotourism casesin Nepal (the BCF case). This forest has been written up in glow-ing terms in a very recent issue of the Smithsonian (41), an ar-ticle written after our fieldwork and analysis was conducted. Thiscase of reforestation is an innovative institution that draws ontraditional approaches to community management of forests, butis representative of the current global trends towards commu-nity-based forest management that are implemented by the stateusing models proposed by international donor agencies. We werenot aware of this case prior to undertaking this exercise. The factthat we did narrow in relatively quickly on this “positive”anomaly gives evidence that our approach could be extremelyuseful in identifying innovative approaches to controlling defor-estation processes. We should acknowledge too that a similarapproach could be taken to identify “negative” anomalies car-rying relevant information as well.

While we think this approach is innovative and useful, somearguments could be made against the approach and we shouldacknowledge its limitations. Several readers of earlier drafts hadquestions about the efficiency parameters of the approach andone questioned whether this approach is cheaper than the tradi-tional method of going to the field and talking to local officialsabout innovations in the region. In terms of cost estimates, therapid reconnaissance fieldwork took one of the authors and twoNepalese colleagues about a week. Another author spent approxi-mately 3 more weeks conducting the case study research. Im-age processing costs vary depending on capabilities. We usedexisting image processing software (ERDAS Imagine) and pur-chased Landsat images from EROS Data Center in the USA forapproximately USD 800 for both images. But, in some instancesone can get paper prints of multispectral color composite im-ages fairly cheap from remote sensing agencies—we know, forinstance, that the National Remote Sensing Agency of India pro-vides this service. Part of the reason we used a very simple visualcomparison was specifically to keep costs as low as possible andto provide a relatively simple technique.

While costs could vary substantially depending on the con-text, we estimate that this research probably cost USD 2500 (ex-cluding airfare, our salaries as university researchers, and the“hidden” costs of our previous work and knowledge on Nepal).As for the question of whether it would be cheaper to simplytalk to officials in the field, we think the satellite images add animportant “neutral” piece of information. Certainly discussionsin the field could yield some potentially interesting cases butthere may be bias in responses or, in some cases, locations thatpeople may not want to advertise for one reason or another. Wethink the satellite image approach helps quickly to review a broadlandscape and provides independent information and thereforewell worth the costs.

Perhaps the biggest limitation to this study relates to case se-lection bias and its impacts on causal inference. Following thework of Geist and Lambin (32), we investigated a theoreticalmodel of deforestation where the dependent variable—the tra-jectory of landcover change between 1989 and 2000—is a func-tion of proximity to roads, population growth, demographic vari-ables, cultural variables, and economic, policy and institutionalattributes. We sampled based on the dependent variable as cap-tured by the satellite images and focused on 4 trajectories of in-terest: i) 1989 dense forest or vegetation to 2000 dense forestor vegetation; ii) 1989 agriculture to 2000 forest or vegetation;


iii) 1989 development to 2000 forest or vegetation; and iv) 1989soil to 2000 forest or vegetation and ignored other trajectoriesof lesser interest (e.g. 1989 agriculture to 2000 agriculture; 1989development to 2000 development; 1989 agriculture to 2000 de-velopment, etc.). It is well known that limiting the range of vari-ation of the dependent variable leads to biased inferences incausal variables. Interestingly, in qualitative research, this meansthat the true causal effects of explanatory variables of interest(e.g. economic or institutional attributes, for example) will onaverage, be larger than what the researcher is led to believe (42).But we also sampled cases based on some known values of theindependent variables of population and proximity to roads,which may introduce additional bias (42).

The impact of this case selection strategy is that we have tobe careful about making statements about independent variablesbeing the cause of the forest anomaly. But that was not our pri-mary objective. Our primary objective was to quickly zero-inon areas that potentially have important institutional arrangementconfigurations and to document them. This approach appears tohave worked very well. Moreover, since we know from thesecases that they do not follow what deforestation theory predictsbased on the important proximate causes of proximity to roadsand population growth dynamics, this lends strong support to thehypothesis that the institutional arrangements in place are influ-ential in the result.

The approach we have developed could therefore help toquickly generate or refine hypotheses on what types of institu-tional arrangements lead to what kinds of outcomes. This couldthen feed larger studies undertaking random sampling approachesthat provide for more variation in dependent and independentvariables, and can lead to definitive answers about relationshipsbetween these variables. Additionally, further refinements couldbe made to the approach to introduce randomization into theprocess. The researcher could conduct a change analysis at abroad spatial scale, characterize various patches of change tra-jectories, and then visit a sample of them using a stratified ran-dom selection approach. Such an approach would help addressthe causal inference issue.

What is interesting from our findings is that the anomalies wediscovered fall into 3 institutional categories: i) command andcontrol state forest reserves (i.e. “fences”); ii) profitable privateactivities (i.e. the plantations); and iii) common property com-munity institutions. This supports the contention that any oneof these broad types of institutional arrangements could yield thesame results (i.e. conservation and regeneration of natural re-sources), but that a deeper institutional analysis is required tounderstand the conditions under which it leads to an equitablesharing of resources. While both state protectionism and privateplantations have the potential to promote regrowth and gener-ate resources, they are, by their very nature, not necessarily eq-uitable forms of resource-sharing.

Another limitation relates to our earlier discussion of “grain”and scale of analysis. In this case we were studying broad (spa-tial) scale deforestation, defined as the existence or loss of for-est canopy. Closed forest canopy is only one measure of the con-dition of a forested area. Also, while Landsat TM images witha resolution of 28.5 m by 28.5 m are appropriate for tracing thephenomenon of deforestation at a grain size of a loss of treestands, this approach may not provide the information contentneeded to understand certain land-cover change phenomena. Forinstance, in this analysis using simple color composites, we couldnot readily distinguish between Sal forest or Sissoo plantations.

Other techniques, such as NDVI (43, 44) or spectral mixtureanalysis (45, 46) may provide better analytic information than avisual comparison, but would also require more image process-ing steps (e.g. radiometric calibration; 45). In some instances,Landsat TM may not provide the required spatial or spectral reso-lutions to capture the grain of the phenomenon of interest. This

satellite platform may not provide sufficient information to un-derstand change in understory vegetation or perhaps even inquestions of change in forest species diversity. To do a spatialinventory of these types of phenomena, another satellite platformwith hyperspectral images (such as AVIRIS), or high spatialresolution (such as IKONOS) might be more valuable.

Another limitation of the Landsat TM sensor spatial resolu-tion is that tree species composition cannot be distinguished atthis scale (44, 47). For instance, in this analysis, we were sur-prised to see what appeared to be a dense Sal forest on the bor-der of an agricultural settlement in the 2000 image in the areacorresponding to Figure 3, Area 3. However, in our field visitwe found all the valuable timber Sal trees were selectively har-vested from this forest patch. This was not apparent from a visualobservation of the image, and is a limitation of sensor spatialresolution.

But just as there are limitations to the process we present herewe also see further opportunities. First, there is a possibility tobetter refine the method of identifying where forest anomaliesreside. Many locations have excellent digital information onroads and topography, at a finer spatial scale available to us inNepal, as well as other GIS layers (e.g. census data) that cap-ture other causes of deforestation, and could help refine thismethodology. Directly relevant to this is the recent work beingdone by many researchers trying to develop models of land use/land cover change that include more of the proximate and un-derlying drivers (see for example ref. 48). A landscape gener-ated by a particular model could be readily compared to a re-cent satellite image or classification map to identify locationsfor rapid assessment.

Second, especially in foraging societies, the identification ofanomalies where forest cover appears to be growing back willpotentially mean that the patterns in forest product use may haveshifted, geographically, to another location. In some of our pre-vious work in the region, we identified such a case, where thereare apparent geographic shifts in foraging behavior away fromoptimal foraging in response to institutional configurations (49).Future research could involve the search for innovative institu-tional anomalies such as we have done, but then also looking atthe potentially negative consequences of changes in forest coverin adjacent regions.

Finally, this analysis searched for anomalies in forest standmanagement. However, the concepts presented here could ac-tually be applied to locate innovative environmental managementendeavors at different, perhaps broader, spatial scales. For ex-ample, instead of looking for anomalies in deforested stands, wecould have conducted an analysis at a broad spatial extent thatlooked to identify watersheds that appear to have maintainedhigher proportions of forest canopy over time or perhaps usingvarious landscape ecology metrics. This could help identify wa-tershed anomalies, with high population densities but that havebeen able to better manage at the watershed scale. This idea thushas potential for identifying innovative efforts of ecosystem man-agement at broader spatial extents rather than the forest standlevel as we have done here.


Charles Schweik is assistant professor in the Department ofNatural Resources Conservation and the Center for PublicPolicy and Administration at the University ofMassachusetts, Amherst, USA. His address: Department ofNatural Resources Conservation, 217 Holdsworth Hall,University of Massachusetts, Amherst, Amherst, MA 01003,USA.E-mail: [email protected]

Harini Nagendra is Asia Research Coordinator at the Centerfor the Study of Institutions, Population and EnvironmentalChange at Indiana University, Bloomington, USA. Heraddress: 696, 15th Cross, J.P. Nagar 2nd Phase, Bangalore560078, India.E-mail: [email protected]

Deb Ranjan Sinha is a graduate student in the Departmentof Geography, University of Illinois, Urbana-Champaign,USA. His address: 950 Main Street, Graduate School ofGeography, Worcester, MA 01610, USA.E-mail: [email protected]

References and Notes1. Mackenzie, F.T. 1998. Our Changing Planet: An Introduction to Earth System Sci-

ence and Global Environmental Change. Prentice Hall. Upper Saddle River, NJ.2. Heywood, V. (ed.) 1995. Global Biodiversity Assessment. Cambridge University Press.

Cambridge.3. Kondratyev, K.Ya. 1998. Multidimensional Global Change. Wiley/PRAXIS Series in

Remote Sensing. Chichester.4. Ives, J.D. and Messerli, B. 1989. The Himalayan Dilemma: Reconciling Development

and Conservation. Routledge. London.5. Netting, R.M. 1981. Balancing on an Alp. Cambridge University Press. New York.6. Ostrom, E. 1986. An agenda for the study of institutions. Public Choice 48, 3–25.7. Ostrom, E. 1990. Governing the Commons: The Evolution of Institutions for Collec-

tive Action. Cambridge University Press. New York.8. McKean, M.A. 1992. Management of traditional common lands (Iriaichi) in Japan. In:

Making the Commons Work: Theory, Practice and Policy. Bromley, D.W., Feeny, D.,McKean, M., Peters, P., Gilles, J., Oakerson. R., Runge, C.F. and Thompson, J. (eds).ICS Press. San Francisco, pp. 63–98.

9. McKean, M.A. 1992. Success on the commons: A comparative examination of insti-tutions for common property resource management. J. Theor. Politics 4, 247–282.

10. Thompson, J.T. 1992. A Framework for Analyzing Institutional Incentives in Commu-nity Forestry. Food and Agriculture Organization of the United Nations, Forestry De-partment, Via delle Terme di Caracalla. Rome.

11. Agrawal, A. 1994. Rules, rule making and rule breaking: Examining the fit betweenrule systems and resource use. In: Rules, Games and Common Pool Resources. Ostrom,E., Gardner, R. and Walker, J. (eds). University of Michigan Press. Ann Arbor, pp.267–282.

12. Blomquist, W. 1992. Dividing the Waters: Governing Groundwater in Southern Cali-fornia. ICS Press. San Francisco.

13. Prakash, A. and Hart, J. 1999. Globalization and Governance. Routledge. London.14. Featherstone, M. (ed.) 1990. Global Culture. Sage. Thousand Oaks, CA.15. Abramson, P.R. and Inglehart, R. 1995. Value Change in Global Perspective. Univer-

sity of Michigan Press. Ann Arbor, MI.16. Tomlinson, J. 1999. Globalization and Culture. University of Chicago Press. Chicago.17. Zald, M.N. 1999. Transnational and international social movements in a globalizing

world. In: Organizational Dimensions of Global Change: No Limits to Cooperation.Cooperrider, D.L. and Dutton, J.E. (eds). Sage. Thousand Oaks, CA.

18. Cerny, P.G. 1997. The Dynamics of Political Globalization. Government and Opposi-tion 32, 251–274.

19. Prakash, A. and Hart, J. 2000. Coping with Globalization. Routledge. London.20. Prakash, A. and Hart, J. 2000. Responding to Globalization. Routledge. London.21. Agrawal, A., Britt, C. and Kanel, K. 1999. Decentralization in Nepal: A Comparative

Analysis. Oakland, CA: ICS Press.22. Gilmour, D.A. 1990. Resource availability and indigenous forest management systems

in Nepal. Soc. Nat. Resources 3, 145–158.23. Messerschmidt, D.A. 1987. Conservation and society in Nepal: Traditional forest man-

agement and innovative development. In: Lands at Risk in the Third World: Local-Level Perspectives. Little, P.D., Horowitz, M.M. and Nyerges, A.E. (eds). WestviewPress. Boulder, CO. pp. 373–397.

24. McKean, M.A. 2000. Common property: What is it, what is it good for, and what makesit work? In: People and Forests: Communities, Institutions and Governance. Gibson,C.E., McKean, M.A. and Ostrom, E. (eds). MIT Press. Cambridge, MA.

25. Müller-Böker, U. 1999. The Chitwan Tharus in Southern Nepal: An EthnoecologicalApproach. Philip Pierce (Translator). Franz Steiner Verlag. Stuttgart.

26. Matthews, S.A., Shivakoti, G.P. and Chhetri, N. 2000. Population forces and environ-mental change: Observations from western Chitwan, Nepal. Soc. Nat. Resources 13,763–775.

27. Guneratne, A. 1994. The Tharus of Chitwan: Ethnicity, Class and the State in Nepal.PhD Diss. University of Chicago, USA.

28. Elder, J.A. 1989. Agriculture Change and Disparities of Wealth and Income AmongResettled Farmers in Nepal. PhD Diss. Cornell University, USA.

29. Nepal, S.K. and Weber, K.E. 1994. A buffer zone for biodiversity conservation: Vi-ability of the concept of Nepal’s Royal Chitwan National Park. Environ. Conserv. 21,333–341.

30. Schweik, C.M., Adhikari, K. and Pandit, K.N. 1997. Land-cover change and forest in-stitutions: A comparison of two sub-basins in the Southern Siwalik Hills of Nepal.Mount. Res. Devel. 17, 99–116.

31. Schweik, C.M. 1998. The Spatial and Temporal Analysis of Forest Resources and In-stitutions. PhD Diss. Center for the Study of Institutions, Population and Environmen-tal Change, Indiana University, USA.

32. Geist, H.J. and Lambin, E.F. 2001. What Drives Tropical Deforestation? LUCC Re-port Series No. 4. University of Louvain.

33. Bhandari, B. 1985. Landownership and Social Inequality in the Rural Terai Area ofNepal. PhD Diss., University of Wisconsin-Madison, USA.

34. Griffin, D.M., Shepherd, K.R. and Mahat, T.B.S. 1988. Human impact on some for-ests of the Middle Hills of Nepal Part 5: Comparisons, concepts and some policy im-plications. Mount. Res. Devel. 8, 43–52.

35. Gilmour, D.A. and Fisher, R.J. 1991. Villagers, Forests and Foresters. Sahayogi Press.Kathmandu.

36. Varughese, G. and Ostrom, E. 2001. The contested role of heterogeneity in collectiveaction: Some evidence from community forestry in Nepal. World Devel. 29, 747–765.

37. Jackson, W.J., Tamrakar, R.M., Hunt, S. and Shepard, K.R. 1998. Land-use changesin two middle hill districts of Nepal. Mount. Res. Devel. 18, 193–212.

38. Lambin, E.F. 1994. Modeling Deforestation Processes: A Review. Trees Series B Re-search Report. Office of Official Publications of the European Community. Luxem-bourg.

39. Shivakoti, G.P., Axinn, W.G., Bhandari, P. and Chhetri, N.B.1999. The impact of com-munity context of land use in an agriculture society. Popul. Environ. 20, 191–213.

40. Jensen, J.R. 2000. Remote Sensing of the Environment: An Earth Resource Perspec-tive. Prentice Hall. Upper Saddle River, NJ.

41. Seidensticker, J. 2002. Tiger tracks. Smithsonian 32, 62–69.42. King, G., Keohane, R.O. and Verba, S. 1994. Designing Social Inquiry: Scientific In-

ference in Qualitative Research. Princeton University Press. Princeton, NJ.43. Nagendra, H. and Gadgil, M. 1999. Biodiversity assessment at multiple scales: Link-

ing remotely sensed data with field information. Proc. Natl Acad. Sci. USA 96, 9154–9158.

44. Nagendra, H. 2001. Using remote sensing to assess biodiversity. Int. J. Remote Sens.22, 2377–2400.

45. Schweik, C. and Green, G.M. 1999. The use of spectral mixture analysis to study hu-man incentives, actions and environmental outcomes. Soc. Sci. Comp. Rev. 17, 40–63.

46. Schweik, C. and Thomas, C.W. 2002. Using remote sensing for evaluating environ-mental institutions: A habitat conservation example. Soc. Sci. Quart. 83, 244–262.

47. Nagendra, H. and Gadgil, M. 1999. Satellite imagery as a tool for monitoring speciesdiversity: An assessment. J. Appl. Ecol. 36, 388–397.

48. US EPA 2000. Projecting Land-Use Change: A Summary of Models for Assessing theEffects of Community Growth and Change on Land-Use Patterns. EPA/600/R-00/098.

US Environmental Protection Agency, Office of Research and Development. Cincinnati,OH.

49. Schweik, C.M. 2000. Optimal foraging, institutions and forest change: A case fromNepal. Environ. Monitor. Assess. 62, 231–260.

50. Support for this research was provided under NSF grant # SBR-9521918 and from theCenter for Public Policy and Administration at the University of Massachusetts,Amherst. The authors wish to thank the following individuals for their help and an-swering our questions patiently during fieldwork: Arun Rijal at KMTNC-Kathmandu,Shankar Chaudhary and Ram Kumar Aryal at KMTNC-Sauraha, Satyanarain Chaudharyat NIDS-Tandi, Giridhari Chaudhary in Sauraha, Bishnu Aryal, Govinda Adhikari,Prakash Dhakal and Tam Bahadur Tamang at BCF FUG, Madhav Acharya, Shiv PrasadSharma, Ambika Paudel, Ravi Timilsina, Arun Gupta and Saroj Koirala at the ChitwanDFO, Keshav Adhikari and Phanendra Thapa at IAAS-Rampur, Pralad Yonzon at Re-sources Himalaya-Kathmandu and Krishna Bahadur Shrestha at the Ministry of Forestand Soil Conservation-Kathmandu. We are also grateful to Mukunda Karmacharya,Birendra Karna, Sudil Acharya and Shiva Adhikari for their invaluable assistance inthe field. Finally, we thank Paul Turner, Arun Agrawal, Clark Gibson, Ted Webb, CleveWillis and two anonymous referees for some excellent suggestions and insights on ear-lier drafts. Of course, all remaining errors are entirely ours.

51. First submitted 11 Febr. 2002. Revised manuscript received 23 Aug. 2002. Acceptedfor publication 24 Aug. 2002.

Charles M. Schweik, Harini Nagendra and Deb Ranjan Sinha ...ifri/Publications/R031-26.pdf · are...

Documents

Transcript of Charles M. Schweik, Harini Nagendra and Deb Ranjan Sinha ...ifri/Publications/R031-26.pdf · are...