forest cover change analysis - nepal -low quality

8/6/2019 Forest Cover Change Analysis - Nepal -Low Quality

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All rights Reserved. No part of this publication may be reproduced, stored in a retrieval

system or transmitted in any form or by any means, electronic, mechanical, photocopying

or otherwise, without the prior permission of the publisher, in this case SDC, Kathmandu

or Intercooperation Nepal.

Photos:

Fritz BergerSurya Kumar Maharjan

Cover:

Rabin Raj Niraula

Design:

Bidur Phuyal

9841202861

Tek Shrestha

Printing:

Print and art ServicePutalisadak, Kathmandu

Ph: 4244419, 4239154, 9849142874Email: [email protected]

Publisher:

Nepal Swiss Community Forestry Project, SDC, Intercooperation

Nepal, 2011

NSCFP, 2011.

ISBN : 978-9937-2-3647-8

The views expressed in this report are those of the authors and do not necessarily represents

those of SDC or Intercooperation. Any ambiguous statement if found is requested to be

perceived with kind consideration.

Authors:Rabin Raj Niraula( [email protected] )Surya Kumar Maharjan( [email protected] )


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Summary

The general notion that the forest cover has increased with the introduction of

Community Forestry Program has been fully supported by the findings of photo

monitoring of the forests along the Lamosanghu - Jiri road corridor. To verify this

results, NSCFP, using the Landsat TM imageries of 1990 and 2010, has undertaken

a Remote Sensing and Geographic Information System based quantitative analysis

of forest cover change in Dolakha district. The study focused on the three clusters

(Bhimeshwor, Singati and Thulopatal) of Dolakha district covering a total area of

27,902.03 ha in 10 VDCs. The study results showed that the forest density has

improved in all three studied clusters between 1990 and 2010. The rate of conversion

of sparse forest into dense forest has been found between 1.13 - 3.39 % per year.Similarly, the rate of conversion of non -forest area into forest has been found between

1.11 - 1.96 % per year . Further, the study also compared the patterns of forest cover

change among different management regimes such as community forests, government

forests and private forests, which showed that the rate of conversion of non-forest

areas into forests in the community managed forests is higher than that in the

government forests and the private forests.

This publication summarizing the results of the current study is expected to benefit

everyone those who are interested in community forestry and its impact on forest

cover. The current study - though has been limited in its scope from its geographic

coverage point of view - has provided a good indication of positive impact ofcommunity forestry on the forest cover of the area. Further extension of similar

analysis in the other parts of the project districts and Nepal as a whole is undoubtedly

necessary for assessment of general impact of community forestry on forest cover

change.

S

um

m

ary

Authors

Forest Cover Change Analysis in Dolakha District 1990-2010 i


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Acknowledgements

We would like to take an opportunity to extend our sincere gratitude to all thoseindividuals and organizations without whose untiring support; this study would not

have been possible. To two of our technical experts Mr. Rabin Raj Niraula and Mr.

Surya Kumar Maharjan for coordinating field work, carrying out analysis and putting

the results of the study together to bring them as this publication; ICIMOD for

cooperation and providing forest boundary data of community forests of Charnawati

watershed; Mr. Hammad Gilani and Mr. Him Lal Shrestha, ICIMOD for technical

advices and support during the analysis process; the staffs of District Forest Office,

Dolakha especially DFO Kedar Nath Dahal for their constant cooperation; GPS survey

team whose hard work made forest boundary survey possible; the local CFUGs, the

FECOFUN, the staffs of NSCFP, Kathmandu and Dolakha for their cooperation and

support; and last but not the least to Dr. Jane Carter; Dr. Bharat Kumar Pokharel, Mrs.

Rudriksha Rai Parajuli, Mr. Brahma Dhoj Gurung and Mrs. Usha Dahal for their

invaluable comments and suggestions.

Nepal Swiss Community Forestry Project

Ack

nowledgem

ents

II Forest Cover Change Analysis in Dolakha District 1990-2010


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1. Introduction 1

1.4. Objectives 2

2. Study area 3

3. Methodology 4

3.1. Data collection 4

3.1.1. Image acquisition 4

3.1.2. Aerial photographs 4

3.1.3. Digital layers of topographic maps 4

3.1.4. Global Positioning System (GPS) survey 5

3.2. Data analysis 5

3.2.1. Image classification 5

3.2.2. Verification and post classification: 6

3.2.3. Forest boundary mapping 7

3.2.4. Map analysis and statistical data management 7

3.3. Limitations of the study 7

4. Results 9

4.1. Land cover status of the study area 9

4.2. Forest cover change 1990-2010 14

4.3. Overall forest cover change 1990-2010 15

4.4. Forest cover change in community forests and other forests 18

4.5. Forest cover change in community forests, government forests and

private forests of Laduk VDC 22

4.5.1. Land cover status of Laduk VDC by management regime 22

4.5.2. Forest cover change in Laduk VDC by management regime 24

5. Drivers of forest cover change 26

5.1. Forest improvement 26

5.2. Forest gain / Increase in forest area 29

5.3. Forest degradation 30

5.4. Forest loss / Decrease in forest area 32

6. Conclusion 33

7. Bibliography 34

Annex 35

Table ofContents

Forest Cover Change Analysis in Dolakha District 1990-2010 III


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List of Figures

Figure 1 : Khorthali Community Forest, Charikot in 1989 and 2010 2

Figure 2 : Study area map showing locations of three study clusters in Dolakha district 3

Figure 3 : Toposheet index map of Nepal 5

Figure 4 : Flowchart showing detailed methodology 8

Figure 5 : Land cover maps of the study area A. Bhimeshwor cluster; B. Singati cluster and C.

Thulopatal cluster 11

Figure 6 : Land cover status of Bhimeshwor cluster 13

Figure 7 : Land cover status of Singati Cluster 13

Figure 8 : Land cover status of Thulopatal cluster 13

Figure 9 : Forest cover change in Bhimeshwor cluster (1990 - 2010) 15

Figure 10 : Forest cover change in Singati cluster (1990 - 2010) 16

Figure 11 : Forest cover change in Thulopatal cluster (1990 - 2010) 16

Figure 12 : Forest cover change in community forests and other forests by cluster (1990 - 2010) 20

Figure 13 : Forest cover change in Laduk VDC (1990-2010) 24

Figure 14 : Forest cover change in Suspa Chhyamawati VDC (1990-2010) 27

Figure 15 : Reclamation of landslide areas and river banks along Charnawati River, Bhimeshwor cluster

between 1990 and 2010 28

Figure 16 : Trees along the river banks, Charnawati River, Bhimeshwor cluster (2011) 28

Figure 17 : Trees in the terrace raisers of farmland, Ramkot, Bhimeshwor Municipality (2011) 29

Figure 18 : Reclamation of abandoned land, Makaibari, Bhimeshwor Municipality (2011) 30

Figure 19 : Forest loss due to uncontrolled and unplanned road construction between 1990 and 2010 in

Kamalamai CFUG, Laduk VDC, Singati cluster 31

List of Tables

Table 1 : Details of Landsat TM imageries used for the study 4

Table 2 : Details of topographic maps used for the study 5

Table 3 : Land cover classification scheme 6

Table 4 : Land cover status by cluster (1990 and 2010) 12

Table 5 : Forest cover change classification scheme 14

Table 6 : Forest cover change by cluster (1990 -2010) 17

Table 7 : Forest cover change in community forest and other forests by cluster (1990 - 2010) 21

Table 8 : Land cover status of Laduk VDC by management regime (1990 - 2010) 23

Table 9 : Forest cover change in Laduk VDC by management regime (1990 - 2010) 24

IV Forest Cover Change Analysis in Dolakha District 1990-2010


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1.Introduction

The history of formalized State forest management in Dolakha district goes back to 1962 that is much earlier thanthe inception of Community Forestry Program in Nepal. The earlier forestry activities were focused on regeneration

of degraded forests; afforestation of government land and improvement of fodder resources. Later coming to the

late 1970s, it was realized that local peoples participation is crucial for the efficient management of forests in rural

Nepal, where they are an integral part of livelihoods. Consequently, Panchayat Forest (PF) and Panchayat Protected

Forest (PPF) were enforced by then single party Panchayat political and administrative system that could be taken

as an early but limited form of community forestry activities in Nepal. Later in 1988, the Nepal Government

introduced the Master Plan for Forestry Sector (MPFS), which highlighted the possibility of handing over 61% of

Nepals forests to local management as community forests. The MPFS 1988 also foresaw that the Community Forestry

Program would be mainly implemented in hills of Nepal, as one important, but not the only forest management

option. Later for the further development of Community Forestry Program in Nepal, the Nepal government made

special provisions for community forests in Section 5 of Forest Act 1992 and Section 4 of Forest Regulations 1994.

Following the Forest Regulations 1994, Community Forestry Directives were introduced in 1995. Since then, the

Community Forestry Program is continuously being implemented in the hilly districts of Nepal, Dolakha district

being one of them.

The 1990s was the decade during which the Community Forestry Program took real momentum in Nepal. It was

the same decade in which the Nepal Swiss Community Forestry Project (NSCFP) started its activities in Dolakha

district. NSCFP that has been in operation for the last two decades (19902011) is phasing out by the end of June

2011. The project support to Dolakha has already phased out in 2010 with only minor activities continuing to the

end of the project. NSCFP, during last two decades, working under multi-partnership approach, i.e. working together

with local District Forest Office (DFO) and its other local partners, has made a substantial contribution for the

development of community forestry program in its project districts and ultimately in Nepal. Much has changed in

development thinking and also development needs over the last two decades. This is clearly reflected in the waythe project focus shifted over time from being primarily technical and environmental in early years, to focusing

more importantly on social needs especially poverty alleviation and promotion of equity and good governance -

in later years.

In general, it is perceived that the forest cover has increased with the increase in the plantation and management

of forests, and subsequent improved natural regeneration in the community forests with the introduction of Community

Forestry Program (Fig. 1). Supporting this general notion, photo monitoring of Lamosanghu - Jiri road corridor has

effectively demonstrated the visible improvement in the forest cover in the area over the past few decades (Pokharel

and Mahat, 2009). However, this is only a qualitative analysis and a detailed quantitative analysis to assess the

overall change in forest cover is lacking. With the above background, NSCFP carried out a Remote Sensing (RS)

and Geographic Information System (GIS) based quantitative forest cover change analysis to assess the rate of change

of forest cover in Dolakha district over past two decades i.e. 1990 and 2010, as a part of its process of overall projectanalysis and documentation.

Forest Cover Change Analysis in Dolakha District 1990-2010 1


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2 Change Analysis in Dolakha District 1990-2010

Figure 1: Khorthali Community Forest, Charikot in 1989 and 2010

adapted from Pokharel and Mahat, 2009.

1989

2010

1.1. Objectives

Using Landsat data of 1990 and 2010; and Remote Sensing (RS) and Geographic Information System (GIS)

technologies, the study intended

1) To prepare digital forest cover maps of the year 1990 and 2010,2) To prepare digital forest boundary maps of community forests and other identifiable forests in the study

area, and

3) To find the patterns of forest cover change between 1990 and 2010 in the study area. The patterns of forest

cover change were analyzed by management regime i.e. forest cover change in community forests and other

forests. Further, a small sub-sample (Laduk VDC) was used to carry out management regime-wise comparison

of forest cover change patterns in community forests, government forests (i.e. national forests other than

community forests) and private forests.

2 Forest Cover Change Analysis in Dolakha District 1990-2010


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2. Study area

Figure 2: Study area map showing locations of three study clusters in Dolakha district. Location of

Dolakha district in Nepal is shown in inset.

The study focused in three clusters (project defined specific geographic areas comprised of few local political units)namely Bhimeshwor, Singati and Thulopatal of Dolakha district. Bhimeshwor cluster is comprised of a municipality:

Bhimeshwor municipality and four Village Development Committees (VDCs): Boch, Lakuridanda, Magapauwa and

Suspa Chhyamawati; Singati cluster is comprised of three VDCs: Laduk, Lamidanda and Suri; and Thulopatal cluster

is comprised of two VDCs: Hawa and Thulopatal. The study thus focused on those nine VDCs and a municipality

of Dolakha district (Fig. 2). Dolakha district was chosen for this study because it contains some of the most mature

Community Forest User Groups (CFUGs), with areas of forest that have been managed as community forests for

well over ten years although, of course, there are also areas of forest that have been handed over more recently.

Broadly speaking, the community forests of Bhimeshwor cluster have been under local management for the longest

time followed by those of Thulopatal and Singati clusters, although a differentiated analysis according to when

handover took place was not included in this study it being a too complicated variable.



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Table 2: Details of topographic maps used for the study.

SN Toposheet ID

1 2785 08

2 2786 01

3 2786 02

4 2786 03

5 2786 05 (A,B,C,D)

6 2786 06

7 2786 07

8 2786 09 (A,B)

9 2886 13

Figure 3: Toposheet index map of Nepal

3.1.4. Global Positioning System (GPS) survey

It is required by the law that all community forests are surveyed before they are handed over to the designated

CFUG for management. However, it is not regular practice to conduct such surveys using GPS rather the

chain and compass method is normally used. Although in some cases a sketch map appears to have been

submitted, a rapid field test of existing forest boundary maps indicated that they are not sufficiently reliable

to allow direct digitization by comparison with other maps. Consequently, field collection of digital information

of forest boundaries became necessary.

GPS survey was intensively applied for the collection of digital information of forest boundaries in the study

area. For this, 21 local individuals were trained to use GPS. Trained individuals were then mobilized in teamsof 2 persons each for the forest boundary survey. Geographic information thus acquired i.e. latitude and

longitude information of forest boundaries of all the surveyed forests in the study area was further processed

to produce forest boundary maps. The forest boundary survey was limited to the forests which had not been

thus surveyed at the time of study. In case of the forests which were already digitally surveyed the forest

boundary maps were acquired from the respective sources, International Centre for Integrated Mountain

Development (ICIMOD) being one of the main sources of such information. The forest boundary maps of

community forests in Charnawati watershed, which covers major portions of Bhimeshwor Municipality, Boch,

Lakuridanda and Magapauwa VDCs, were acquired from ICIMOD. The community forest maps included in

the operational plans available at the DFO, Dolakha served as basis for verifying the forest boundary maps

prepared from the GPS survey data.

3.2. Data analysis

3.2.1. Image classification

The geo-referenced Landsat TM imageries acquired from the USGS-EROS archive consist of seven layers, one

for each of seven spectral bands. These seven layers were stacked to produce seven band composite imageries.

These imageries were then classified using ENVI 4.2 application (ITT Visual Information Solutions, Colorado).

As the study team was highly familiar with the field situations and there was plenty of secondary information



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Table 3: Land cover classification scheme

SN Land cover classes Description

1 Dense forest Areas covered with trees with>40 % crown cover

2 Sparse forest Areas covered with sparsely distributed trees with 10 - 40 % crown cover

3 Agriculture Cultivated areas, settlements, roads and other areas not included in other classes

4 Grassland Open areas with short vegetation, areas may even have few scattered trees

5 Barren land Areas with no vegetation cover, stock quarry, stony areas, uncultivated

agricultural lands6 Sand Sand and gravel deposits along the riverbanks

7 Water bodies Perennial rivers, ponds, lakes; rivers without water or very little water may

appear in barren land category and small streams with trees along the bank

may appear in sparse forest

3.2.2. Verification and post classification:

Supervised maximum likelihood classification resulted into "salt and peppery" classified land cover maps as

a result of pixel by pixel classification taking into account spectral variability of pixels during the classification

procedure. Such salt and peppery classified land cover maps were generalized using 3x3 majority smoothing

filter. As direct field verification of classified land cover map of the year 1990 was not possible, they were

verified using the samples generated from digital layers of topographic maps acquired from DoS, Nepal (such

samples were checked visually with available aerial photographs before using them for verification). In case

of the classified land cover map for year 2010, direct field verification was carried out. Thus, the majority

of samples were collected from the field while some were also generated from the high resolution GeoEye

satellite data available on Google Earth. Field verification was focused on those areas where classification

of land cover was dubious. Field verification results showed that the classification results were satisfactory

(overall accuracy 89.96 % and 89.37 % for 1990 and 2010 respectively: confusion matrices are given in

about the field situations available in NSCFP, a supervised maximum likelihood classification was used.

Imageries were classified emphasizing six main categories of land cover i.e. Forest; Grassland, Agriculture,

Barren land, Sand and Water bodies. Forest areas were further classified into Dense forest (with >40 %

crown cover) and Sparse forest (with 10 40 % crown cover) (see also Table 3). For this, several training

samples were selected for each land cover category. The training samples for the year 1990 were generatedfrom digital layers of topographic maps acquired from DoS, Nepal and such samples were also checked

visually with available aerial photographs. While the training samples for the year 2010 were mainly

collected from the field and some were generated from the high resolution GeoEye satellite data available

on Google Earth.

In case of 1990 imagery, clouded and snow covered areas were initially classified into separate classes. Later

such cloud class was removed by merging it to the actual land cover classes by checking them with available

aerial photographs and topographic maps. Snow classes were reclassified as grassland (based on the field

experience: the snow covered high altitude areas are only covered by snow during the winter and usually

serve as pastures during the summer). Shadow class in both year imageries were removed by first classifying

shadow as a separate class and later merging such shadow class to the actual land cover classes by checking

it with available aerial photographs, topographic maps and GeoEye satellite data available in Google Earth.



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Projection: Transverse Mercator Scale Factor: 0.9996

False Easting: 500000 Latitude of Origin: 0

False Northing: 0 Linear Unit: Meter

Central Meridian: 87 Datum: D WGS 1984

3.3. Limitations of the study

Forest Act 1993 and Forest Regulations 1995 are the two main legal documents for managing Nepal's forest

resources. As per the Forest Act 1993, there are two main categories of forest in Nepal based on land ownership

i.e. National forest and Private forest. Further based on the use right and the management regimes, National

forest is sub-divided into five categories namely, Government-managed forest; Protected forest, Community

forest, Leasehold forest and Religious forest. The study originally aimed to survey the boundaries of all these

different categories of forest separately to make management regime-wise comparison of forest cover change.

As per the target, all community forests except for two (one each in Hawa and Thulopatal VDCs) were

surveyed, and forest boundary maps were prepared. The boundary of those two community forests could not

be surveyed because of some practical difficulties in the field. In addition to the community forests, other

identifiable forests, notably: government-managed forests (33), religious forests (2), leasehold forests (13) and

private forests (76) were also surveyed and forest boundary maps were prepared. However, all existing

government-managed forest, leasehold forest, religious forest and private forest could not be surveyed becausein most of the cases the boundaries of such forests could not be properly identified in the field. Thus, instead

of treating other identifiable forests that were surveyed during the study separately, they were grouped as

other forests and management regime-wise comparison of forest cover change pattern was limited to community

forests against all other forests. Further, Laduk VDC, where there were forest of all three management regimes

i.e. community forests, government forests (i.e. national forests other than community forests) and private

forests in adequate number was taken as a sub-sample to make management regime-wise comparison of forest

cover change patterns in community forests, government forests and private forests.

Annex 8 and 9). DFO, Dolakha and key informants from the studied VDCs were consulted further to verify

the classification results and the discrepancies found from such consultations were corrected in the final maps

before incorporating them in GIS application for further analysis.

3.2.3. Forest boundary mappingThe geographic information (i.e. latitude and longitude information) of forest boundary points collected from

GPS survey were first processed in MapSource application (Garmin International, Inc., Kansas) before exporting

them to Google Earth. Google Earth with high resolution GeoEye satellite data served as a good platform for

visually verifying the geographic information collected during the GPS survey and for creating forest boundary

layers. Forest boundary layers were then exported to ArcGIS 9.3 application (ESRI, California) where they

were developed into forest boundary polygons. Boundary mapping has a displacement resolution of 10-100m.

3.2.4. Map analysis and statistical data management

Classified land cover maps and forest boundary maps were incorporated in ArcGIS 9.3 application for further

analysis. All the required data calculations and forest cover change analysis were carried out in ArcGIS 9.3

application.



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A. Bhimeshwor cluster

4.1. Land cover status of the study area

Landsat imageries of the year 1990 and 2010 were classified using ENVI 4.2 application to produce land cover

maps with seven land cover classes namely, Agriculture, Barren land, Dense forest, Grassland, Sparse forest, Sand

and Water bodies (Fig. 5). Among all three clusters considered for the study, Singati cluster has the highest forest

cover (77.43 %) followed by Thulopatal cluster (64.55 %) and Bhimeshwor cluster (61.60 %) in 2010. The overall

forest cover in the study area is 67.86 % which is way more than the national average of 39.6 % (FAO, 2009).

1990

2010


4. Results


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B. Singati cluster

1990

2010



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C. Thulopatal cluster

Figure 5: Land cover maps of the study area:

A. Bhimeshwor cluster; B. Singati cluster and C. Thulopatal cluster.

1990

2010



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The comparison of land cover maps of 1990 and 2010 showed the considerable change in Agriculture, Dense forest,

Grass land and Sparse forest in all three studied clusters between 1990 and 2010 (Table 4). However, there is only

negligible change in Barren land, Sand and Water bodies. Dense forest has increased substantially while Sparse

forest has decreased in all three clusters. Between 1990 and 2010, Dense forest has increased by 3,491.53 ha in

Bhimeshwor cluster, 2,634.77 ha in Singati cluster and 434.63 ha in Thulopatal cluster while Sparse forest hasdecreased by 2,052.87 ha in Bhimeshwor cluster, 1,815.38 ha in Singati cluster and 519.75 ha in Thulopatal cluster

(Table 4). Agriculture has increased in all three clusters. Between 1990 and 2010, Agriculture has increased by

495.33 ha in Bhimeshwor cluster, 31.17 ha in Singati cluster and 309.22 ha in Thulopatal cluster (Table 4). Unlike

Agriculture land, Grassland has decreased in all three clusters. Grassland has decreased by 1,875.65 ha in Bhimeshwor

cluster, 843.00 ha in Singati cluster and 224.14 ha in Thulopatal cluster (Table 4).

Table 4 : Land cover status by cluster (1990 and 2010)

Land cover class Area (ha) Net Land cover change (ha)

1990 2010

Agriculture 4723.05 5218.38 495.33

Barren land 91.65 39.35 -52.30

Dense forest 2151.26 5642.80 3491.53

Grassland 2286.52 410.87 -1875.65

Sparse forest 5525.22 3472.35 -2052.87

Sand 1.70 0.16 -1.54

Water bodies 19.09 14.60 -4.50

Agriculture 1894.11 1925.28 31.17

Barren land 8.58 6.13 -2.46

Dense forest 2690.26 5325.03 2634.77

Grassland 852.46 9.45 -843.00

Sparse forest 3266.99 1451.61 -1815.38

Sand 0.92 1.16 0.24

Water bodies 38.57 33.23 -5.34

Agriculture 932.29 1241.52 309.23

Barren land 0.18 0.36 0.18

Dense forest 643.81 1078.44 434.63

Grassland 524.92 300.78 -224.14

Sparse forest 2250.29 1730.54 -519.75

Sand 0.00 0.00 0.00

Water bodies 0.15 0.00 -0.15

Bhimeshworcluster

Singaticlu

ster

Thulo

patalcluster


-ve sign represents trend of change


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In Bhimeshwor cluster, between 1990 and 2010, sparse

forest decreased from 37 % to 24 % and dense forest

increased from 15 % to 38 %. Similarly, agriculture increased

from 32 % to 35 % and grassland decreased from 15 % to

3 %. However, changes in other classes were negligible

(Fig. 6).

Figure 6: Land cover status of Bhimeshwor cluster.

Inner circle represents the land cover status of 1990 and

outer circle represents that of 2010.

In Singati cluster, between 1990 and 2010, sparse forest

decreased from 37 % to 17 % while dense forest increased

from 31 % to 61 %. Similarly, grassland decreased from 10

% to less than 1 %. However, changes in agriculture and

other classes were negligible (Fig. 7).

In Thulopatal cluster, between 1990 and 2010, sparse

forest decreased from 52 % to 40 % while dense forest

increased from 15 % to 25%. Unlike agriculture that

increased from 21 % to 28 %, grassland decreased from

12 % to 7 %. However, changes in other classes were

negligible (Fig. 8).

Figure 7: Land cover status of Singati cluster.

Inner circle represents the land cover status of 1990

and outer circle represents that of 2010.

Figure 8: Land cover status of Thulopatal cluster.

Inner circle represents the land cover status of

1990 and outer circle represents that of 2010.



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4.2. Forest cover change 1990 - 2010

In order to analyze the forest cover change, the land cover maps of 1990 and 2010 were first reclassified into Dense

forest (D), Sparse forest (S) and Non-forest areas (N). Non-forest areas include all land cover classes except Dense

forest and Sparse forest i.e., Agriculture, Barren land, Grassland, Sand and Water Bodies. Such reclassified land

cover maps of 1990 and 2010 were overlaid as shown in the forest cover change matrix below to detect the forest

cover change in ArcGIS 9.3 application.

where, D stands for Dense forest,

S for Sparse forest,N for Non-forest,

DD for Dense forest in 1990 that remained unchanged in 2010,

DS for Dense forest in 1990 that got degraded to Sparse forest in 2010,

DN for Dense forest in 1990 that got deforested to Non-forest in 2010,

SD fo Sparse forest in 1990 that got upgraded to Dense forest in 2010,

SS for Sparse forest in 1990 that remained unchanged in 2010,

SN for Sparse forest in 1990 that got deforested to Non-forest in 2010,

ND for Non-forest in 1990 that got forested to Dense forest in 2010,

NS for Non-forest in 1990 that got forested to Sparse forest in 2010 and

NN for Non-forest in 1990 that remained unchanged in 2010.

The nine forest cover change pattern obtained from the overlay analysis were reclassified into six forest cover

change classes following the classification scheme shown in Table 5.

Table 5: Forest cover change classification scheme

SN Forest cover change class Definition

1 Improved forest Sparse forest upgraded to Dense forest SD

2 New forest area Non-forest forested to Sparse or Dense forest ND, NS

3 Unchanged forest Dense or Sparse forest that remained unchanged DD, SS

4 Degraded forest Dense forest degraded to Sparse forest DS5 Deforested area Dense or Sparse forest deforested to Non-forest DN, SN

6 Unchanged non-forest Non-forest that remained unchanged NN


Forest coverchange pattern


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4.3. Overall forest cover change 1990 - 2010

The overlay analysis showed that forest density has improved in all three clusters between 1990 and 2010 and that

in Bhimeshwor and Singati clusters, the forest area has also increased considerably. However, in Thulopatal cluster,

there has been a small decrease in total forest area between 1990 and 2010 (Fig. 9 - 11 and Table 6).

Figure 9: Forest cover change in Bhimeshwor cluster (1990 - 2010).

Black circle marks Khorthali Community Forestry (the same forest shown in Fig. 1),

Charikot, one the oldest CFUGs in Dolakha. The area was mostly covered with sparse

forest in 1990 which now have grown into a dense pine forest, a good example of forest

improvement.



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Figure 10: Forest cover change in Singati cluster (1990 - 2010).

Figure 11: Forest cover change in Thulopatal cluster (1990 - 2010).



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In Bhimeshwor cluster, 3,012.93 ha (i.e. 55 % of sparse forest in 1990) has shown a marked improvement in density

and 2,110.23 ha (i.e. 30 % of non-forest in 1990) has become forested. Similarly, in Singati cluster, 2,216.97 ha

(i.e. 68 % of sparse forest in 1990) has shown a marked improvement in density and 1,097.82 ha (i.e. 39 % of non-

forest in 1990) has become forested. In Thulopatal cluster, 509.31 ha (i.e. 23 % of sparse forest in 1990) has shown

a marked improvement in density and 323.28 ha (i.e. 22 % of non-forest in 1990) has become forested. Unlike inBhimeshwor and Singati clusters, in Thulopatal cluster, when the absolute changes in the forest area were compared,

the amount by which the forest area has increased (323.28 ha) could not quite keep up with the amount by which

the forest area has decreased (406.80 ha) - although the amount by which sparse forest has changed into dense

forest (509.31 ha) was higher than the amount by which dense forest has changed into sparse forest (88.56 ha).

Thus, despite some small annual forest loss, the proportion of dense forest is increasing in Thulopatal cluster. The

overall slight reduction in forest area and relatively higher rate of deforestation (i.e. forest is disappearing at the rate

of 0.70 % per year) and forest degradation (i.e. dense forest is changing into sparse forest at the rate of 0.69 %

per year) found in Thulopatal cluster are probably due to the increasing practice of felling Alnus nepalensis

trees on the private land to fulfill the increasing demand of veneer enterprises established in Dolakha and its

neighboring districts.

Table 6: Forest cover change by cluster (1990 - 2010).

Land cover class 1990 Forest cover change (ha)

Land cover Area (ha) Improved forest New forest area Degraded forest Deforested area

Sparse forest 5525.22 3012.93

Non-forest 7122.01 2110.23

Dense forest 2151.26 -161.82

Forest 7676.48 -671.04

Change (%) 54.53 29.63 -7.52 -8.74

Rate of change

(% per year) 2.73 1.48 -0.38 -0.44


Non-forest 2794.63 1097.82

Dense forest 2690.26 -14.31

Forest 5957.25 -264.06

Change (%) 67.86 39.28 -0.53 -4.43

Rate of change

(% per year) 3.39 1.96 -0.03 -0.22


Non-forest 1457.54 323.28Dense forest 643.81 -88.56

Forest 2894.10 -406.80

Change (%) 22.63 22.18 -13.76 -14.06

Rate of change

(% per year) 1.13 1.11 -0.69 -0.70

Bhimeshworcluster

Singaticluster

Thulopatalcluste

r




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4.4. Forest cover change in community forests and other forests

The study also compared the patterns of forest cover change in community forests and other forests. For this, a total,

11,117.47 ha of community forests, excluding two community forests that were not surveyed, were grouped under

community forests and 3,115.99 ha of other identifiable forests that were surveyed during the study were grouped

under other forests. The comparison showed that conditions of both the community forests and the other forests

have considerably improved over past two decades i.e. 1990 and 2010 in all three studied clusters (Fig.12 and

Table 7).

Forest cover change in community forest (Bhimeshwor cluster)

Forest cover change in other forest (Bhimeshwor cluster)



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Forest cover change in community forest (Singati cluster)

Forest cover change in other forest (Singati cluster)



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Other forest


Non-forest 332.05 312.93


Forest 1795.56 -16.17

Change (%) 89.65 94.24 -0.19 -0.90

Rate of change

(% per year) 4.48 4.71 -0.01 -0.05

The higher rates of forestation (i.e. the rate of change of non-forest areas into forests) and forest improvement (i.e. the rate

of change of sparse forests into dense forests) found in all three clusters indicate that the improvement in forest cover has

been achieved both in terms of forest area and forest density in both categories of forests (Fig. 12 and Table 7). This

improvement can be attributed to the increased awareness among local people regarding forest issues, and in particular

to the effective management and monitoring of the community forests and their neighboring other forests by the user

groups. The rates of forestation within the community forests have been found substantially higher than that in the other

forests in all three clusters (Table 7). In addition to that, the rates of deforestation in community forests have been found

slightly lower than that in other forests in all three clusters (Table 7). The implication is that CFUGs are more successful

in managing and monitoring their forests and preventing encroachment than other management regimes.



Community forest



Forest 5706.70 -54.55

Change (%) 68.49 91.63 -7.53 -0.96

Rate of change

(% per year) 3.42 4.58 -0.38 -0.05

Other forest


Non-forest 126.48 70.03


Forest 449.59 -34.33

Change (%) 43.71 55.37 -4.18 -7.64Rate of change

(% per year) 2.19 2.77 -0.21 -0.38

Community forest


Non-forest 397.22 387.37


Forest 2967.83 -7.22

Change (%) 72.44 97.52 -0.50 -0.24

Rate of change

(% per year) 3.62 4.88 -0.02 -0.01

Bhimeshworcluster

Singaticluster


Table 7: Forest cover change in community forests and other forests by cluster (1990 - 2010).


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Community forest


Non-forest 33.41 32.01


Forest 761.60 -4.44

Change (%) 46.71 95.79 -5.29 -0.58

Rate of change

(% per year) 2.34 4.79 -0.26 -0.03

Other forest




Forest 359.58 -16.26Change (%) 45.90 62.79 -6.26 -4.52

Rate of change

(% per year) 2.29 3.14 -0.31 -0.23

Thulopatalcluster

4.5. Forest cover change in community forests, government forests and private forests of

Laduk VDC

4.5.1. Land cover status of Laduk VDC by management regime

For effective comparison of the patterns of forest cover change among different management regimes, Laduk

VDC, where adequate number of community forests (6 community forests with cumulative area of 1,221 ha),

Government forests (5 Government forests with cumulative area of 270.19 ha) and private forests (14 private

forests with cumulative area of 89.01 ha) were present, was taken as a sub-sample. Laduk VDC has an area

of 2,683.95 ha out which 62 % is under forest cover. The comparison of land cover maps of 1990 and 2010

showed the considerable change in Dense forest, Grassland and Sparse forest in overall Laduk VDC and also

in the forests under all three management regimes (Table 8). However, there is only negligible change in

Barren land, Sand and Water bodies. Unlike other land cover classes, Agriculture has decreased considerably

in private forests indicating the conversion of private agriculture lands into forests. Dense forest has increased

substantially while Sparse forest has decreased in overall Laduk VDC and also in the forests under all three

management regimes.




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Table 8: Land cover status of Laduk VDC by management regime (1990 - 2010).

Land cover Area (ha) Net Land cover change (ha)

1990 2010

Overall

Agriculture 720.74 728.38 7.64

Barren land 4.31 6.01 1.70

Dense forest 427.34 1227.78 800.44

Grassland 287.12 0.30 -286.82

Sparse forest 1234.04 716.03 -518.01

Sand 0.09 0.00 -0.09

Water bodies 10.31 5.46 -4.85

Community forest

Agriculture 0.00 0.00 0.00

Barren land 0.09 1.76 1.67

Dense forest 329.49 854.92 525.43

Grassland 185.40 0.00 -185.40

Sparse forest 705.08 363.84 -341.25

Sand 0.09 0.00 -0.09

Water bodies 0.85 0.48 -0.37

National forest

Agriculture 19.68 18.61 -1.07

Barren land 0.25 0.00 -0.25

Dense forest 74.37 202.48 128.11

Grassland 20.57 0.00 -20.57

Sparse forest 152.88 49.04 -103.84

Sand 0.00 0.00 0.00

Water bodies 2.44 0.06 -2.38

Private forest

Agriculture 23.75 6.14 -17.61

Barren land 0.00 0.00 0.00

Dense forest 8.51 50.40 41.89

Grassland 6.44 0.00 -6.44

Sparse forest 50.31 32.48 -17.84

Sand 0.00 0.00 0.00

Water bodies 0.00 0.00 0.00




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4.5.2. Forest cover change in Laduk VDC by management regime

The overlay analysis showed that forest area

as well as forest density have improved in

overall Laduk VDC as well as in the forests

under all three management regimes between1990 and 2010 (Fig. 13 and Table 9).

The rates of forestation and forest improvement

have been found to be considerably higher

than that of forest degradation and deforestation

in overall Laduk VDC and in the forests under

all three management regimes (Table 9).

Moreover, the rate of forestation within the

community forests has been found substantially

higher than that in the forests under other

management regimes and the rate ofdeforestation in the community forests has been

found slightly lower than that in the forests

under other management regimes (Table 9).

This further suggests that CFUGs are more

successful in managing and monitoring their

forests and preventing encroachment than other

management regimes.



Overall


Non-forest 1022.58 391.00


Forest 1661.38 -107.26

Change (%) 59.43 38.24 -1.13 -6.46

Rate of change

(% per year) 2.97 1.91 -0.06 -0.32

Figure 13: Forest cover change in Laduk VDC (1990 - 2010).

Table 9. Forest cover change in Laduk VDC by management regime (1990 - 2010).



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Community forest



Forest 1034.57 -2.13

Change (%) 70.51 96.98 -1.12 -0.21

Rate of change

(% per year) 3.53 4.85 -0.06 -0.01

National forest




Forest 227.25 -10.13

Change (%) 76.78 76.50 -0.23 -4.46Rate of change

(% per year) 3.84 3.83 -0.01 -0.22

Private forest




Forest 58.82 -0.90

Change (%) 66.94 87.11 -0.75 -1.52

Rate of change

(% per year) 3.35 4.36 -0.04 -0.08




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The study results showed that forest cover has changed considerably in the study area between 1990 and 2010.In all three studied clusters, forest density has increased and in Bhimeshwor and Singati clusters forest area has also

increased considerably. However, in Thulopatal cluster, there has been a slight reduction in forest area. Based on

the personal field experience of the study team, interviews with the local stakeholders and document reviews, the

following factors have been identified as the main drivers of forest cover change in the study area.

5.1. Forest improvement

5.1.1. Community based forest management

The results of this study clearly demonstrate the positive effect that community forestry has had on overall

forest area as well as forest density in the three clusters of Dolakha district that were investigated. Under

community forest program, user groups take responsibility for managing and monitoring forest areas accordingto a management plan, which includes measures for fuelwood, timber and fodder extraction on an equitable

basis. This is aided by the natural conditions which support ready natural regeneration. An important point

in this respect is the control of livestock grazing in community forests and promotion of stall feeding, which

has had a remarkably positive impact on vegetative cover.

5.1.2. Less dependency/efficient use of forest resources

The social context has changed dramatically over past two decades in Dolakha district. Urban residents did

not have access to biogas; improved cooking stoves, liquefied petroleum gas in 1990s and they had to rely

almost entirely on fuelwood for cooking. The current easy access to biogas, improved cooking stoves and

liquefied petroleum gas for many people - certainly those with road access and some monetary income - has

considerably reduced dependency on forests for fuelwood. The establishment of saw-mills has also increased

the efficiency of timber processing, meaning that where such facilities are available timber use in house

construction has become less wasteful. In the past, it was typical to harvest mainly pole-sized trees instead

of large mature trees and to use the whole tree as a pillar or beam in construction. Thus, it was common to

fell several trees to build a single house in the past. However, with improved roads and transportation facilities,

saw-milling has become accessible even to many of those people living in remote parts of the study area,

meaning that only large mature trees are harvested as per community forest operational plans, with one tree

sometimes providing sufficient timber for several houses.

5.1.3. Decline in slash and burn practice

Slash and burn was practiced in Dolakha district in the past, but was already on the decline twenty years ago

due to limited suitable land and better alternative opportunities, including out-migration. Such opportunities

have greatly increased, and family members are moving far and wide in search of better livelihood options.

Furthermore, for those staying behind, better access to fertilizer and improved seeds means that intensive

farming is far more attractive than slash and burn. In this regard, Gyan Lal Subedi, a resident of Serabesi, said,

"in the past my family and I used to cultivate the wasteland adjacent to our farmland and it was still difficult

for us to produce enough food for our family. However, at present, we are just cultivating our farmland and

we are producing enough food for our family. Once we stopped cultivating the wasteland, trees started

regenerating in the area and currently we have a small patch of Uttis (Alnus nepalensis) forest in our wasteland,

which we can sell anytime to make good money".


5. Drivers of forest cover change


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5.1.4. Decline in forest fire incidences

Forest fire can have considerable influence on forest cover, especially during the dry season. Occasionally,

forest fires can cause severe damage to the regeneration and the existing vegetation. According to local people,

the dense coniferous forest with plenty of mature trees along the upper part of Suspa Chhyamawati VDC was

completely burnt in 1991 (Fig. 14). Some of the burnt trees can still be seen in the area. Following the forestfire, the area remained bare without regeneration for several years. But later, the burnt area was seeded and

protected against grazing in the initiation of local user groups. Consequently, the area is slowly regenerating.

Above that coming to the recent times, the number of forest fire incidences has also decreased noticeably

in Dolakha district -with some occasional small forest fires put out successfully in joint effort of the local

CFUGs.

Black oval marks the dense forest area that was completely burnt in 1991. The area is slowly

regenerating following seeding and protection against grazing.

Figure 14: Forest cover change in Suspa Chhyamawati VDC (1990 - 2010).

5.1.5. Reclamation of landslide areas and river banks

In the past, when substantial areas in the studied clusters had only degraded forest cover, flash floods were

quite common. In some cases these triggered huge landslides. The improved forest cover in the area has

reduced flash floods incidences and associated landslides. In addition, soil conservation measures applied

to the landslide areas and river banks through the support of District Soil Conservation Office (DSCO) has

resulted in -most of the landslide areas and formerly degraded river banks being reclaimed. As can be seen

from the images of Bhimeshwor cluster (Fig. 15), streams that were quite distinct in the 1990 image can hardly

be identified in the 2010 image as most of them have been covered with trees on both the banks of the stream

(Fig. 16). The landslide areas also have disappeared among the flourishing trees (Fig 15).



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Figure 15: Reclamation of landslide areas and river banks along Charnawati River, Bhimeshwor Cluster

between 1990 and 2010.

Black circles & ovals mark the landslide areas along Charnawati river that have disappeared among the flourishing trees

in 2010 Landsat TM imagery.

Figure 16: Trees along the river banks, Charnawati River, Bhimeshwor cluster (2011).

1990

2010



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5.2. Forest gain / Increase in forest area

5.2.1. Plantation

Plantation was a prime activity of the Integrated Hill Development Project (IHDP). By the end of the project

(i.e. by the end of fiscal year 1989/90), over 3,900 ha of government land in Dolakha district had been planted(including over 50 ha of government land planted under the (JMDP) Jiri Multipurpose Development Project).

In addition, NSCFP also provided financial support to the DFO in its initial phases for plantation. During those

years, mainly Pinus spp. was planted in the district, and as a result abundant pine forest can be seen in the

district at present, particularly Pinus caribaea (an exotic pine species) in the vicinity of roads. Both during

and following the plantation, there have been arguments for and against the plantation ofPinus spp. Local

people quite often complained that pine plantations have caused acidification; reduction of the regeneration

of fodder species and also had a negative impact on the water table of the areas, resulting drying up of water

springs. However, others are happy that pine plantations have considerably increased the greenery of their

surroundings and have stabilized the slopes, which otherwise would have slipped or washed away. Sita Ram

Basnet, Ex-chairperson of FECOFUN and a resident of Bhimeshwor Municipality, highlighting the importance

of pine plantation said,

"those who don't have shoes to put on will first look for slippers, and then only for good and comfortable

shoes. In order to cover the bare slopes, some vegetation was needed, pine fulfilled that need, and now there

is greenery everywhere. So, now is the time to think whether to keep pine forest as it is or to go for conversion

and it is always possible to convert currently existing pine forests into mixed species broadleaf forests with

some management interventions".

It was in the beginning of implementation of community forestry program in Dolakha district that plantation

of government land was the prime forestry intervention of the Nepal government and the projects. As implied

in the quote above, CFUGs can now choose how to manage the mature plantations, and slowly convert them

to other species compositions, if required.

5.2.2. Conserving trees in private

land and terrace raisers of

farmland

Partly as a result of public

awareness campaigns on

agroforestry, but simply out of

recognized need, people started

planting and conserving trees

(mainly fodder trees) on their

private lands. Those who owned

wasteland (gullies and otheruncultivated spaces) used such

areas to plant and conserve trees,

whilst others turned to tree

plantation and conservation on

the terrace raisers of their

farmland. Consequently, trees

ou t s ide the fo re s t haveFigure 17: Trees in the terrace raisers of farmland, Ramkot, Bhimeshwor

Municipality (2011).



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considerably increased in the study area. In some places, the trees along the terrace raisers of the farmland

have grown so big and thick that the farmland can easily be mistaken for forest (Fig. 17). Highlighting this

situation, Gokul Prasad Neupane, Treasurer, Simpani CFUG, Bhimeshwor Municipality, said,

"it used to be very difficult in the past, when there were no fodder trees in our farmland because we had to

spend lots of energy and time to collect fodder needed for the livestock and at present, when there are enough

fodder trees in our farmland, it is still very difficult because the trees in farmland have grown so big and thick

that they are casting shade to the agricultural crops growing underneath and are adversely affecting the crop

production".

5.2.3. Land abandonment

The study area faced massive outward migration (people moving to cities and abroad in search of secure and

better life) during and following the decade long (1996 - 2006) political conflict. With this increased trend

of outward migration, there was a certain amount of land abandonment in the study area. This concerns only

the marginal lands, as the population was also increasing, most of the productive land remained under

cultivation. Abandoned land that remained uncultivated for some years has been reclaimed by naturalregeneration and is slowly changing into forest (Fig. 18).

Figure 18: Reclamation of abandoned land, Makaibari, Bhimeshwor Municipality (2011).

5.3. Forest degradation

5.3.1. Illegal extraction of timber and fuelwood

Illegal extraction of timber and fuelwood has always been one of the major causes of forest degradation in

the hills of Nepal, where people rely on forests for construction materials and for energy for cooking. In the

past, local people used to extract timber and fuelwood from forest to fulfill their subsistence needs, but coming



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5.4. Forest loss / Decrease in forest area

5.4.1. Forest encroachment

Encroachment of the forest land still occurs in some parts of the study area although it is taking place at much

slower pace than in earlier times. Increase in public awareness through the implementation of communityforestry program in the district and regular monitoring of forest areas by the user groups under community

forestry program have been successful in curbing the rate of encroachment if not controlling it totally in the

study area. With effective management and monitoring of the community forests, the encroachment pressure

has shifted to the remaining national forests. The local people of Serabesi, Boch VDC said,

"a patch of national forest along Ghattekhola River was claimed by an elite in the village some time ago but

now with the combined effort of all the villagers they are planning to get the forest handed over as a

community forest and bring it under community management so that nobody else can claim the forest in

future".

However, in recent times, the practice of providing part of community forest areas for schools and hospitals

is increasing in the name of local development. So far such things are happening in limited scale in certainareas but if it continues, in long run, this may have serious impact on forest cover leading to loss of forest

areas.

5.4.2. Felling trees on private land

There are currently five veneer enterprises in function in Dolakha district. With their establishment, the

demand for timber (especiallyAlnus nepalensis) has skyrocketed. People started getting a good price for

timber that used to be burned as domestic fuelwood in the past. Consequently, they started felling trees on

their private land in a desire to make quick money. Dabal Tamang, one of the owners of Gauri Shankar Wood

Production and Sawmill Pvt. Ltd., Bhimeshwor Municipality said,

"ninety percent of our timber demand at present is met by the timber supplied from the private land and only

10 % comes from the community forests. The complicated administrative process that is involved in extracting

timber from the community forests is discouraging the extraction of timber from them".

Although there are no official figures to support the local claims of high levels of tree felling on private land

in Thulopatal cluster, the current study results showing a slight reduction in forest area support the observation.

However, given thatAlnus nepalensis is fast growing that regenerates naturally, this harvesting should not

be labeled as deforestation -though some control mechanism should surely be developed to check irregularities

involved in such harvesting.



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The current study - that focused on the three clusters of Dolakha district - showed that the forest density has improvedin all three studied clusters and that the forest area has also increased in Bhimeshwor and Singati cluster between

1990 and 2010.

Furthermore, the study also compared the patterns of forest cover change among different management regimes

such as community forests, government forests and private forests, and showed that Community Forest User Groups

are more successful in managing and monitoring the forests and preventing encroachment than other management

regimes with relatively higher rates of forestation and lower rates of deforestation in the community forests.

The study also identified that community based forest management; less dependency/efficient use of forest resources;

decline in slash and burn practice; decline in forest fire incidences; reclamation of landslide areas and river banks;

plantation; conserving trees in private land and terrace raisers of farmland; land abandonment; illegal extraction

of timber and fuelwood; road construction; forest encroachment; and felling of trees on private land are the major

drivers of forest cover change in the study area.

The current study - though has been limited in its scope from its geographic coverage point of view - has provided

a good indication of positive impact of community forestry on the forest cover in the studied clusters. It is, thus,

recommended to carry out similar analysis in other parts of the project districts and Nepal as a whole to assess

general impact of community forestry on the forest cover of Nepal.


6. Conclusion


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FAO. Global Forest Resources Assessment 2010 - Nepal Country Report. 2009.

Hobley, Mary, Jagadish Baral, Narendra Rasaily, and Bihari Shreshtha. Nepal Swiss Community Forest Project - External

Review. 2007.

Pokharel, Bharat Kumar, and Anupama Mahat. Kathmandu to Jiri: A Photo Journey. Nepal Swiss Community Forestry

Project, SDC, Intercooperation, 2009.


7. Bibliography


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Annex


Annex 1. Landsat TM imagery 1990 used for the study.


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Annex 2. Landsat TM imagery 2010 used for the study.



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Annex 3. List of the participants of GPS aided forest boundary survey professional training. These participants

were mobilized in teams of 2 persons each for forest boundary survey.

SN Name Address

1 Roshan K. Shrestha Bhimeshwor Municipality - 1

2 Shambhu Ram Thami Bhimeshwor Municipality - 3

3 Prabin Bhujel Bhimeshwor Municipality - 6

4 Nani Maya Neupane Bhimeshwor Municipality - 10

5 Gyan B. Tamang Boch - 2

6 Ramesh Karki Hawa - 1

7 Mingmar Sherpa Hawa - 1

8 Sushila Shrestha Laduk - 7

9 Samundra Oli Laduk - 8

10 Prem Narayan Joshi Lakuridada - 5

11 Jaganath Khatri Lamidada - 7

12 Lokendra Karki Lamidada - 8

13 Bina Lama Magapauwa - 3

14 Renuka Khadka Suri - 3

15 Hem Kumar Thapa Suri - 5

16 Suresh Khadka Suri - 5

17 Apsara Khadka Suri - 5

18 Mekh B. Khati Sushpa Chhemawati - 6

19 Mohan Raj Siwakoti Sushpa Chhemawati - 9

20 Krishna Khatri Thulopatal - 3

21 Temba Sherpa Thulopatal - 4



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Annex 4. In total there are 111 community forests in the study area. Out of 111 community forests, 68 were

surveyed during the study; for 41 community forests in Charnawati watershed, forest boundary maps were

obtained from ICIMOD; and two community forests, one each in Hawa and Thulopatal namely Khalte CF and

Rambole CF respectively could not be surveyed because of some practical difficulties in the field.

SN Name Area (ha)

Bhimeshwor Municipality

1 Amlekharka CF 6.5978

2 Archale CF 32.8845

3 Banarasi Kadel CF 12.5528

4 Bandevi CF 8.0173

5 Barkhe Dandapari CF 35.3976

6 Bhasmepakha CF 10.934

7 Bhirmuni Devithan CF 5.98288 Bichaur CF 47.714

9 Budha Bhimsen CF 86.9145

10 Charnawati CF 831.55

11 Chatali CF 12.6542

12 Chuche Dhungha CF 8.8984

13 Chyakthali CF 2.9751

14 Chyarchyare CF 9.6663

15 Chyase Bhagabati CF 30.3215

16 Darfu CF 4.4527

17 Devithan CF 43.9406

18 Dhande CF 29.1734

19 Gahate Baghkhor CF 5.544

20 Gaide CF 13.3998

21 Jilu CF 28.0555

22 Kamalamai CF 70.8274

23 Karandi CF 5.6103

24 Khahare CF 16.7311

25 Khorthali CF 174.6341

26 Kupri Salleri CF 42.0316

27 Lamachaur CF 6.7993

28 Mahankal CF 39.3787

29 Majhkharka Lisepani CF 174.1772

30 Mathani CF 28.2834

31 Nagdhunga CF 42.4823

32 Saharpa CF 21.793

33 Setidevi CF 44.3628

34 Shivajang Bhumesthan CF 46.6727

35 Simle CF 61.4077

36 Simpani CF 64.3953

37 Simpani Khahare CF 84.5842

38 Simsungure CF 32.762739 Sitakunda CF 206.3917

40 Siyoudada CF 31.4505

41 Sundari Mai CF 12.976

42 Tahaleshwori CF 53.0421

43 Thangsa Deurali CF 126.1328

44 Tikhatal CF 40.753

45 Tilinchok Rani CF 13.767

Boch VDC

46 Bhitteri CF 572.6294

47 Botle Setidevi CF 201.3324

48 Chittakunda CF 23.6702

49 Dhande Singhadevi CF 300.454

Hawa VDC

50 Jhyamte Dovan CF 133.7023

51 Dumsi Jhyang CF 155.5435

52 Thulogaira Odare CF 118.0624

53 Kalleri CF 31.1018

54 Khalte CF NA

SN Name Area (ha)



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Laduk VDC

55 Chitra Devithan CF 119.658

56 Dambhote CF 92.2727

57 Fiste Dhunga CF 92.2745

58 Gidde Salleri CF 136.2276

59 Kamalamai CF 145.0095

60 Timbu CF 639.5616

Lakuri VDC

61 Dimal CF 38.1955

62 Eklepakha CF 181.4622

63 Napke Yanmara CF 175.346864 Paleko Ban CF 1.4848

65 Sankha Devi CF 277.2826

66 Seti Devi CF 407.3357

67 Thumka Danda CF 37.9715

68 Timure Tinsalle CF 44.2107

Lamidada VDC

69 Chiuri Kharka CF 28.6219

70 Choksa Lampokhari CF 104.0017

71 Choksha CF 1.3667

72 Kattike Beniswara CF 88.6977

73 Kattike CF 58.2684

74 Salle Bhandar Kharka CF 86.5264

75 Sirish Ghari CF 88.3341

76 Thalari Pakha CF 59.2005

77 Thalthale Salleri CF 27.052

Maga VDC

78 Bhakare CF 104.4282

79 Mahabhir CF 50.2606

80 Maithan Harisiddhi CF 28.3341

81 Palung Mahila CF 10.2754

82 Pauwa CF 58.6442

SN Name Area (ha)

83 Pokhari CF 23.6001

84 Ramite CF 13.597

85 Salleri CF 92.01

86 Tharlange CF 203.9683

Suri VDC

87 Bajre Danda CF 294.4381

88 Deurali CF 74.2544

89 Hapunang Tutepani CF 253.5605

90 Jogum CF 116.0761

91 Kabutar Yarpang CF 92.4488

92 Khurung CF 64.064893 Koshenidhi CF 624.8569

94 Kuku Dayale Cfa 9.1695

95 Kuku Dayale CFb 16.8761

96 Messel CF 10.9076

97 Okhreni CF 12.7424

98 Ramite Fungling CF 86.3351

Suspa Chyamawati VDC

99 Bosimba CF 33.5619

100 Damar Thami CF 245.0602

101 Gumpha Mahabhir CF 156.195

102 Jhareni Deurali CF 207.1444

103 Ramite Masandada CF 4.7541

104 Suspa CF 745.1352

105 Thalcha CF 6.7829

Thulopatal VDC

106 Hosinga CF 80.1681

107 Thalari CF 125.6599

108 Bhusune Sungure CF 94.0536

109 Surke Salme Cfa 30.3541

110 Surke Salme CFb 35.7653

111 Rambole CF NA

SN Name Area (ha)



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Annex 6. Community forest boundary map of Singati cluster.

Forest

Cover

Change

Analysis

in

Dolakha

District1990-20

10


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Annex 7. Community forest boundary map of Thulopatal cluster.

Forest

Cover

Change

Analysis

in

Dolakha

District1990-20

10


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Annex 8. Confusion matrix of Landcover classification 1990.

Ground truth pixels

Landcover Barren Water Sand Agriculture Dense Sparse Grassland Total User

land bodies forest forest pixels accuracy(%)

Barren land 68 0 1 0 0 0 0 69 98.55

Water bodies 0 140 5 0 0 0 0 145 96.55

Sand 0 0 37 0 0 0 0 37 100.00

Agriculture 1 2 0 1179 0 30 216 1428 82.56

Dense forest 0 0 0 0 795 7 0 802 99.13

Sparse forest 0 0 0 54 44 929 1 1028 90.37

Grassland 0 0 0 43 0 0 472 515 91.65

Total pixels 69 142 43 1276 839 966 689 4024

Produceraccuracy (%) 98.55 98.59 86.05 92.40 94.76 96.17 68.51

Overall

accuracy (%) (68+140+37+1179+795+929+472)/4024 = 3620/4024 = 89.96

Ground truth pixels

Landcover Barren Water Sand Agriculture Sparse Grassland Dense Total Userland bodies forest forest pixels accuracy

(%)

Barren land 15 0 0 0 0 0 0 15 100.00

Water bodies 0 117 5 0 78 0 0 200 58.50

Sand 0 0 10 0 0 0 0 10 100.00

Agriculture 1 1 0 993 20 0 95 1110 89.46

Sparse forest 0 0 0 38 861 6 241 1146 75.13

Grassland 0 0 0 0 49 229 0 278 82.37

Dense forest 0 7 0 0 59 0 2821 2887 97.71

Total pixels 16 125 15 1031 1067 235 3157 5646Producer

accuracy (%) 93.75 93.60 66.67 96.31 80.69 97.45 89.36

Overall

accuracy (%) (15+117+10+993+861+229+2821)/5646 = (5046/5646) = 89.37

Annex 9. Confusion matrix of Landcover classification 2010.



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forest cover change analysis - nepal -low quality

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