Elsevier

Journal of Environmental Management

Volume 91, Issue 2, November–December 2009, Pages 471-488
Journal of Environmental Management

Determinants of deforestation in Nepal's Central Development Region

https://doi.org/10.1016/j.jenvman.2009.09.016Get rights and content

Abstract

The process of deforestation in the Central Development Region (CDR) of Nepal is diverse in space and time, with rapid deforestation still occurring in areas outside the national parks and wildlife reserves. This paper identifies the spatial driving forces (SDFs) of deforestation in the CDR for 1975–2000 using satellite data of 1975 (MSS), 1990 (TM), and 2000 (ETM+) along with socio-demographic and socioeconomic variables. Radiometrically calibrated satellite images are individually classified into seven distinct classes and merged together to cover the entire CDR. Classification accuracies are also assessed. Areas of land use and cover within the areas of each Village Development Committee (VDC) and municipality represented by GIS polygons are calculated from the classified images by overlaying vector files of 1845 polygons representing sections of VDCs and municipalities in 30–1199 m, 1200–2399 m, 2400–4999 m and >5000 m elevation levels. These elevation levels were estimated from the DEM compiled from 24 ASTER scenes taken on different dates. Only the first three elevation levels are used in the analysis because area >5000 m is under permanent snow cover where human related forestry activities are almost negligible. A transition matrix is generated for 1975–1990 using classified images of 1975 and 1990 and then this product is used to further develop another transition matrix for 1990–2000 with the classified ETM+ 2000 images as the final stage. The GIS polygon layer is overlaid on the transition matrices to calculate deforestation areas for 1975–1990 and 1990–2000. Biophysical and socioeconomic information collected from various sources is then brought into a GIS platform for statistical analyses. Six linear regression models are estimated using SAS; in effect, two models for each elevation range representing the 1975–1990 and 1990–2000 periods of change to identify SDF influences on deforestation. These regression analyses reveal that deforestation in the CDR is related to multiple factors, such as farming population, genders of various ages, migration, elevation, road, distance from road to forest, meandering and erosion of river, and most importantly the conversion of forestland into farmland.

Introduction

Most research on land use and land cover dynamics (LUCD) in the 1970s and 1980s focused on tropical regions of South American countries (Laney, 2004, Nepstad et al., 1999, Whitemore, 1997). With close to half of the world's tropical forests now impacted by human settlement, LUCD research elsewhere has increased considerably since then. This wider LUCD literature suggests that modeling the integration of biophysical and socioeconomic information will help to identify which proximate spatial driving forces (SDFs) are causing deforestation in specific geographic contexts at local and regional-scales (Armenteras et al., 2005, Aspinall, 2004, Chowdhury, 2006, Deininger and Minten, 2002, Ferreira et al., 2006, Pfaff et al., 2007). On the other hand, the LUCD literature doesn't often deal effectively with the implications and outcomes of national-level policies and practices.

Furthermore, much of this literature has been characterized as ‘aspatial’ in that local-level, geographical variations in deforestation patterns are often overlooked at the expense of macro-level generalizations. On specific oversight persists in that LUCD analysis often fails to embed socio-demographic information of the resident populations with biophysical data. Only rarely are interrelationships of such mixes of proximate, causal factors modeled, so that demographic and socioeconomic attributes of populations and their settlement infrastructures- such as distance from roads to the forested areas, and the lengths and areal coverage of roads – together with biophysical features – such as elevation, aspects, slopes, rivers – are analyzed holistically.

The main goal of this paper, therefore, is to utilize the aforementioned ‘embedding’ of socio-demographic and biophysical attributes to enable us to identify which important location-specific factors are influencing deforestation at local, spatio-temporal scales in the Central Development Region (CDR) of Nepal. Elsewhere, attempts have been made to address the effects of SDFs on deforestation by integrating remote sensing data with spatially-referenced biophysical, social and economic information (Chowdhury, 2006, Liverman et al., 1998, Moran and Ostrom, 2005), yet, no such studies have been done in this regard in contemporary Nepal, where so many people still depend on farm and forestry activities. In this landlocked South Asian, country the human ‘nature and environment’ relationships vary widely; depending upon farming practices, the prevailing climate and weather, and this mountainous country's considerable biophysical and topographical diversity. As a result, synergistic effects of SDFs on forestry are most likely to differ at micro-scales. Accordingly, we intend to investigate how these many intersecting factors influence deforestation processes at local and regional, spatio-temporal scales.

To address some of the aforementioned oversights in the current literature on LUCD and deforestation, we utilize three elevation zones of Nepal's Central Development Region (CDR) to re-define (and refine) the three ecological regions that represent the familiar and ‘oft-used’ tripartite physiographic regions of Nepal- the Tarai, hills and mountains. This enables us to incorporate heuristic ‘rules of thumb’ concerning locally-known practices of land and forest management into our analyses so that we can examine how SDFs differentially influence deforestation at these three elevation levels; namely tropical and subtropical (30–1199 m), temperate (1200–2399 m), sub-alpine and alpine (2400–4999 m) zones. We do not include areas with elevations over >5,000 m, because these areas are mostly under snow cover, are of marginal agricultural value and are for all intents and purposes tree-less.

Our approach to this classification of Nepal's CDR into ecological zones differs significantly from the conventional way the CDR –like the country as a whole – has been regionally divided into the mountains, hills and Tarai regions. The conventional approach, which is mainly based on the country's political framework of administrative boundaries, fails to take into account the location-specific SDFs that influence forest management. In contrast, our regional method of dividing the CDR according to common elevation levels enables us to specifically include potentially-significant SDFs such as biophysical factors and ongoing community forestry approaches. Because deforestation is such a complex process, we have re-adjusted our regionalization scheme to take this diversity and complexity into account. Support for this methodological re-adjustment is found among other LUCD researchers, who have even used Mills methods of negotiation (Rindfuss et al., 2007), because factors affecting deforestation vary across culture contexts and geographic locations.

Though it might have been considered more straightforward to use, the conventional approach would not have enabled us to identify SDFs by forest management regimes, nor would it have allowed us to examine how these regimes vary when implemented in various forests in different ecological zones. Since the bio-geochemical cycle is relatively higher within the 30–1199 m elevation that also includes riverside forests of the hills, the Nepal government manages these forest stocks due to their high commercial values. For example, commercial timbers such as, Acacia catechu, Dalbergia sps, Shorea robusta, and Terminalia sps are generally auctioned by the government for revenue generation, and forests where these valuable stocks predominated were rarely handed over to local communities until the early 2000s. Also, if we were to follow the conventional regionalization approach, the majority of the Siwalik/Churia ranges hills, which are around 1200 m in elevation, would be included in the Tarai region, despite the fact that the government's Master Plan for the Forestry Sector in 1988 had recommended that the forests of these ranges should be managed by local communities. Although these forests are within the Tarai region in terms of overall geographical location, not many areas are easily accessible and not all of the timber stocks fetch high commercial values. Our ecological divisional approach separates the Siwalik/Churia ranges from the Tarai region and therefore makes it possible to examine the different synergistic effects of SDFs on deforestation therein. Since the SDFs' influences on forests at lower elevation differ from deforestation and reforestation outcomes at higher elevations (>2400 m), we develop separate models to examine these effects. Accordingly, our regionalization schema takes into account the considerable ecological variations that occur at various elevations in Nepal. By so doing, we follow the example of Phua et al. (2008) who have also used different models to test the effects of SDFs inside and outside national parks in Malaysia. Similar to their case in which the management regimes varied between the two situations, Nepal's also vary in its elevation-determined, ecological zones.

We choose the CDR for several reasons: a) this region represents the country's diverse landscapes ranging from 30 m to 7100 m with elevation zones ranging from tropical, subtropical, temperate, alpine and sub-alpine to snow belts; b) during the 1975–2000 period, this CDR region experienced the most rapid land use and cover change in comparison to Nepal's other four development regions (Far Western, Mid-Western, Western, and Eastern) (Fig. 1b); c) its population density is relatively high in Nepal, estimated to be 293 people/kilometer as compared to the national average of 164 people/kilometer; d) there are rapid social and demographic changes underway due to the location in the CDR of several of the country's major urban administrative centers, especially the Kathmandu Valley where the capital city is located; and e) the first community forestry program that started in Nepal, began in this region in 1978; specifically at the >1200 m elevation level.

Bhattarai (2001) and Bhattarai and Conway (2008) have identified SDFs using aspatial (socioeconomic) and spatial (remote sensing) data for the Tarai's Bara district, one of the 19 districts of the CDR in Nepal. Elsewhere, Chowdhury, 2006, Ferreira et al., 2006, Skole and Tucker, 1993, Sader, 1995, and Soares-Filho et al. (2006) have studied the influences of SDFs on LUCD and their impacts on ecosystems in other countries' forested regions, such as the Yucatan Peninsula region of Mexico and the Brazilian Amazon. Closer to home, Nepal et al. (2007) used econometric modeling to examine linkages between the strength and type of social networks in private forest conservation activities in rural Nepal. However, their modeling effort did not use spatial variables.

A large body of literature has examined and reflected upon the causes of deforestation, but very rarely have the causes of deforestation been examined at various elevation levels with respect to the ecological variation they bring about. The major goals of this article, therefore, are to specifically test the following set of hypotheses concerning the expected influences of SDFs on deforestation's patterns in the CDR of Nepal and how they differ at various elevation levels and ecological zones:

  • a.

    The extent of anthropogenic (human) influences on deforestation and afforestation, assessed from satellite imagery, will vary among tropical and subtropical, temperate and sub-alpine and alpine belts, in large part because of the differences in natural resource bases and human settlement dynamics in these ecological zones.

  • b.

    Community forestry approaches are effective means to conserve and manage forests and thus to preserve forested areas and forest stocks in higher elevation belts.

  • c.

    The higher the elevation the lower the population pressure on forest stocks, but deforestation occurs due to biophysical factors.

We estimate the areas of deforestation from transition matrices and integrate this information at an appropriate local scale of measurement in which the areal units of observation are GIS polygons, which we generate from GIS data records. There are grounds for selective exclusion of GIS polygons in certain districts in the CDR in our modeling estimations, however. Notably, polygon units in the five urban areas of the Kathmandu Valley, namely, Bhaktapur, Kathmandu, Kirtipur, Lalitpur, and Madhyapur Thimi, and one sub-metropolitan area —Birgunj--located in the south near the Indian border – are omitted from consideration due to the nonexistence of forest therein. We also exclude polygons in two national parks and one wildlife reserve from the model estimations because of their strict military protection by the Nepal Army, which prevents incursions or illegal logging and effectively deters deforestation. Finally, we have not included areas above 5000 m in elevation in the analysis because virtually no human-related forestry activity occurs in this very high mountainous zone.

The GIS polygons we do use, however, are all to be found in CDR's conventional administrative districts – VDCs and municipalities – located within the elevation ranges of 30–1199, 1200–2399, and 2400–4999 m. And, as previously mentioned, a major reason for not using VDCs and municipalities as our ‘local unit’ is because their areal reaches sometimes extend into more than one elevation range, so that the types of vegetation and their management systems will constitute a mix of ecological conditions that would confound (and misrepresent) analysis. In our analyses, therefore, we will be using 1915 GIS polygonal units of observation and analysis, which are found between the elevations of 30 m and 4999 m. Regionally, the breakdown within the three ecological zones is the following: 1085 polygons were created for the lowest elevations (30–1199 m), 609 polygons were created for the 1200–2399 m elevation range, and 221 polygons were created for the 2400–4999 m elevation range. Now following this introduction, we next present our theoretical framework and its remote sensing pedigree. Then we detail the study area, deal with the considerable number of data specification issues we face, and finally introduce and specify the land use and land use change models we intend to estimate for the two successive periods of time within our spatio-temporal ‘window'- between 1975–1990, and 1990–2000. The models’ analytical outcomes are then presented, and we summarize our findings on the CDR's deforestation and its determining SDFs in a reflective discussion centered around the testing of three hypotheses The study's major conclusions are then highlighted.

Section snippets

Theoretical background

Analysis of the causes and consequences of deforestation involves complex interrelations because they result from the effects of different driving forces in which some of these forces might be accelerating, while others are decelerating. Increasing population and human settlement encroachments, and road network developments have been identified as accelerating factors for deforestation (Pfaff, 1999, Rudel, 1989). Development of infrastructure often facilitates in-migrants' access to the

Study area

The Central Development Region (CDR) of Nepal (Fig. 1c) sustains 37% of the country's population within 19% of its geographic area and experiences a heightened central role in the nation's overall development because of the location of the country's primary urbanized core and administrative center, the Kathmandu Valley, within its boundaries. Such has been the extent of urbanization and urban sprawl at the expense of rural and non-urban cover in the Valley, however, that this region is omitted

Data issues

This research uses biophysical (land use and land cover, roads, rivers, slopes, aspects, and elevations), socio-demographic and socioeconomic data (population, age group, income, land holding, occupations, migrant status) at an appropriate local scale of measurement and analysis; namely, GIS polygons. These units of analysis are smaller and more statistically valid than the Village Development Committees (VDCs) level, which the central government views as the smallest administrative unit of

Land use and cover dynamics (1975-to-2000)

Land use and land cover classes are derived for 1975, 1990, and 2000 for 30–1199 m, 1200–2399 m, and 2400–4999 m elevation levels. In this integration process, only five classes—mature forest, secondary growth, degraded vegetation, farmland, and bare-land—are used for 1975, 1990, and 2000 (Fig. 6). Mature forest includes areas under trees of all types, such as hardwood (broad leaved) and softwood (conifers) of various ages that were identified based on their reflective properties in bands 3 and 4

The main model

After the integration, our six linear models are estimated utilizing SAS to identify the determinants of deforestation. We utilize 1915 GIS polygon records as our units of analysis, each located within one of the three ecological zones which range between 30 m and 4999 m in elevation. In each elevation level/ecological zone, we consider road accessibility and hydrological influences as space-variant and yet time dependent variables to examine the transitional probabilities for land use and cover

Hypothesis testing

Based on the model outputs (Table 1, Table 2, Table 3), we successively test the three hypotheses we proposed earlier.

Hypothesis I

The extent of human disturbance, assessed from satellite imagery, will vary among tropical and subtropical, temperate and sub-alpine and alpine belts.

Previous work by Muller-Boker (1999) in Chitwan, had concluded that the main causes of deforestation in this lower elevation, tropical forested district were due to the impacts of human settlements. Extending the range of

Conclusion

In this research, we first identified the spatial driving forces (SDFs) of deforestation from a theoretical perspective by reviewing the pertinent deforestation literature and then relating the generalizations derived from this growing body of evidence to the specific cultural context and geographic particularity of the Central Development Region (CDR) of Nepal. Notable, is this region's representativeness as the context where drivers (SDF) of deforestation – the contemporary economic and

Acknowledgements

This research is supported by an Indiana Space Grant, by the Ford Foundation, and a University Research Grant from the Office of Sponsored Programs and the College of Arts, Humanities, and Social Sciences of the University of Central Missouri (19-SU003). This research would not have been completed without data access to NASA's Land Processes Distributed Active Archive Center (LP DAAC) and data support from Principal Scientist, Dr. Chandra Giri of USGS. Special thanks also go to Prof. Nanda R.

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