Technical ReportPDF Available

STATE of NEPAL'S FORESTS

Authors:
  • Forest Research and Training Centre, Nepal
  • Ministry of Forests and Environment, Nepal
GOVERNMENT OF NEPAL
MINISTRY OF FORESTS AND SOIL CONSERVATION
DEPARTMENT OF FOREST RESEARCH AND SURVEY
FOREST RESOURCE ASSESSMENT NEPAL
Publicaon No. 5 December 2015
STATE OF NEPAL'S FORESTS
STATE OF NEPAL’S FORESTS
II
© Department of Forest Research and
Survey, 2015
Any reproduction of this publication in
full or in part should mention the title and
credit DFRS/FRA.
Citation
DFRS, 2015. State of Nepal's Forests.
Forest Resource Assessment (FRA) Nepal,
Department of Forest Research and
Survey (DFRS). Kathmandu, Nepal.
ISBN: 978-9937-8896-3-6
Published by
Department of Forest Research and Survey
P.O. Box: 3339, Babarmahal
Kathmandu, Nepal
Tel: 977 1 4220482, Fax: 977 1 4220159
Email: info@dfrs.gov.np
Web: www.dfrs.gov.np
Cover Photographs
Front cover: Terai Sal forest of Rautahat
District, Nepal
Back cover: High altitude forest of Kalikot
District, Nepal
STATE OF NEPAL’S FORESTS i
The contribuon of forests to the well-being of humankind is enormous and wide-ranging.
Forests play an important role in ensuring food security, combang rural poverty and providing
decent livelihoods to the people. Forests also oer green growth opportunies and provide vital
environmental services such as cleaning air and water, conserving biodiversity and watersheds
and addressing climate change impacts. By providing essenal goods and services, sustainably
managed forests ulmately contribute to sustainable development. Therefore, forests and their
role have also been recognised in the United Naon's sustainable development goals.
Reliable and up-to-date informaon on the state of forest resources is crucial for supporng
policy formulaon, strategic planning, nancial investment and sustainable forest management.
The current forest policy of Nepal also recognises the need for updang the informaon of
the country's forest resources. Forest Resource Assessment (FRA) Nepal project was a bilateral
cooperaon between the Governments of Nepal and Finland. The project aimed to conduct the
naonal level forest assessment for updang informaon on forest resources of Nepal.
While this report summarises the results of the FRA project, greater details are provided in
physiographic region-wise reports. I believe that the implementaon of naonal FRA and
producon of all these reports are the major steps forward in the forestry sector of Nepal.
On behalf of the Government of Nepal, I am thankful to the Government of Finland for providing
nancial and technical support to implement the FRA Nepal project. I would also like to
acknowledge the eorts of the Department of Forest Research and Survey in implemenng the
project and all the stakeholders contribung for the successful compleon of the project.
Finally, I would like to assure that the Ministry of Forests and Soil Conservaon is commied
to instuonalise periodic FRA system and ulise the updated forest resource informaon
for forestry sector policy-making, planning and sustainable forest management. I hope the
informaon disseminated by this report will be benecial in full extent to all decision makers,
planners, academicians, students and other professionals working in the eld of natural resource
management.
Thank you.
Agni Prasad Sapkota
Minister
Ministry of Forests and Soil Conservaon
MESSAGE
STATE OF NEPAL’S FORESTS
ii
FOREWORD
Naonal level forest resource informaon is important for policy-making, strategic level planning,
and internaonal reporng by government. To generate this informaon, the Government of
Nepal implemented the Forest Resource Assessment Nepal (FRA Nepal) project from 2010 to
2014 with support from the Government of Finland.
This naonal report presents the results of the forest resource assessment of the enre country.
It provides a wide range of informaon including forest cover, growing stock, biomass, and
forest carbon. In addion, there are separate physiographic region-wise detailed reports for
Terai, Churia, and Middle Mountains, and a combined report for High Mountains and High Himal
physiographic regions.
I would like to acknowledge the eorts of the Department of Forest Research and Survey for
execung the Forest Resource Assessment project. I appreciate the hard work of all those
involved in planning, eld inventory, data analysis, mapping, report wring and other supporve
work related to the implementaon of FRA Nepal project.
I would also like to take this opportunity to express my sincere gratude to the Government of
Finland for providing technical and nancial support to accomplish this important work.
I am condent that the capacity enhancement of our instuons and personnel during the FRA
Nepal project would be useful for undertaking forest resource assessments in future.
I believe that the results of this study will be useful not only in policy-making, strategic planning
and internaonal reporng but will also serve as baseline informaon for future forest resource
assessments of the country.
Uday Chandra Thakur
Secretary
Ministry of Forests and Soil Conservaon
STATE OF NEPAL’S FORESTS iii
Like any major undertaking, the Forest Resource Assessment Nepal project was the collecve eort
of many. In parcular, I appreciate the eorts of the following people who, in their various capacies,
contributed in the implementaon of the project. Some names are repeated to recognise the person's
role in various capacies.
Project Steering Commiee: Yuvaraj Bhusal, Chhabi R. Pant, Keshab P. Bhaarai, Naveen K. Ghimire,
Krishna C. Paudel, Ganesh R. Joshi, Sharad C. Poudel, Madhav P. Acharya, Gopal K. Shrestha, Harihar
Sigdel, Annapurna N. Das, Sahas M. Shrestha, Biswo N. Oli, Braj K. Yadav, Gopal K. Upadhyaya, Megh
B. Pandey, Krishna P. Acharya, Bharat P. Pudasaini, Yam B. Thapa, Tika R. Adhikari, Pem N. Kandel,
Mukunda P. Ghimire, Kiran R. Sharma, Ramesh Shakya, Hasta B. Thapa, Deepak K. Kharal, Yam P.
Pokharel, Kailash Pokharel, Abhoy K. Das, Chudamani Joshi, Kari Leppanen, Pekka Seppala, Tuomo A.
Komaki, Micheal D. Hawkes.
Project Management Commiee: Sahas M. Shrestha, Ramesh Shakya, Hasta B. Thapa, Deepak K.
Kharal, Yam P. Pokharel, Pem N Kandel, Tuomo A. Komaki, Michael D. Hawkes, Shree K. Gautam,
Keshab R. Goutam, Buddi S. Poudel, Sharad K. Baral, Rajesh Malla, Sabitri Aryal.
Validaon Commiee: Krishna P. Acharya, Gopal P. Bhaarai, Jisnu M. Bhaarai, Kamal Ghimire,
Rabindra Maharjan, Ramesh Basnet, Shree K. Gautam, Yam P. Pokharel.
Technical Team: Kalpana Shrestha, Shiva Khanal, Bimal K. Acharya, Raj K. Giri, Thakur Subedi, Gayatri
Joshi , Junu Shrestha, Anu Rajbhandari, Sangeeta Shakya, Bishwo B. Pudasaini, Tika R. Pokharel, Padmira
Dangol, Ashok Chaudhary, Manju Ghimire, Milan Dhungana, Ananda Khadka, Amul K. Acharya, Shova
Paudel, Ian Thomas, Ulrike Nocker, Bhuwan K. Sharma, Ajay Pandey, Anish Joshi, Ashwin Dhakal,
Hari Pokharel, Jukka Alm, Heikki Parikka, Kari Korhonen, Kiran Timalsina, Basanta Gautam, Pramod
Sharma, Saurav Shrestha, Ajay Mishra, Tanya Laila, Tuija Suihkonen, Sajana Maharjan, Saroj Koirala,
Sushil Lamichhane, Puspa Pandey, Tej B. Basnet, Anil Shrestha, Bijaya R. Poudel, Sita Aryal.
Field Crew Team: Shree K. Gautam, Keshab R. Goutam, Buddi S. Poudel, Sharad K. Baral, Rajesh
Malla, Sabitri Aryal, Gopal P. Gautam, Shiva Khanal, Devendra L. Karna, Rajan Regmi, Basanta Sharma,
Rajendra K. Basukala, Naresh K. Karna, Bimal K. Acharya, Raj K. Giri, Milan Dhungana, Manju Ghimire,
Thakur Subedi, Kamal R. Aryal, Rajeev K. Jha, Amul K. Acharya, Khem Bishwokarma, Bishnu P. Dhakal,
Kajiman Tamang, Ram K. Bhandari-Chhetri, Dayanidhi Aryal, Khila N. Dahal, Ram A. Yadav, Jagadish
Regmi, Prem Sapkota, Mahadeep Pokharel, Bhuwaneshwor Chaudhary, Sujit K. Jha, Amardev P.
Yadav, Govinda P. Poudel, Surendra Shrestha, Babu R. Aryal, Sailendra K. Misra, Narendra P. Guragain,
Mahinarayan Chaudhary, Suvash K. Sharma, Mukunda Adhikari, Nabin Chalise, Achal Dhungana,
Rambalak Yadav, Bechu Yadav, Ramesh K. Giri, Mun B. Raut, Niraj Dangi, Jaya Tripathi, Govinda Thapa,
Bindu Subedi, Mijan Regmi, Ashish Tiwari, Kamal Ghimire, Deepak Aryal, Sunil Dhungana, Bijaya
K. Yadav, Birendra K. Yadav, Deepak Lamichhane, Lajmina Joshi, Kabita Sharma, Manisha Pandey,
Babita Maharjan, Bhawani Shrestha, Ganga D. Bhaa, Md. Sajjad Alam, Dambar B. Karki, Shekhar
C. Bha, Ramesh Gautam, Santosh Labh, Binita Sahi, Khum B. Thapa Magar, Salik R. Sigdel, Suman
Dhakal, Usha Adhikari, Binod K. Basnet, Shree H. Bhaarai, Yam K. Basnet, Rupa Subedi, Bina Wagle,
Madhabes Pathak, Rajan P. Paudyal, Rabin Suwal, Injun Acharya, Ajit Tumbahamphe, Umesh Khanal,
Jivan Paudel, Raj Tiwari, Renu Napit, Niraj Thapa, Nabin Bhaarai, Suprem Prajapa, Yub R. Khadka,
ACKNOWLEDGEMENTS
STATE OF NEPAL’S FORESTS
iv
Debendra Bhandari, Anil Shah, Kashi Yadav, Kashi N. Chaudhary, Bijaya K. Yadav, Krishna K. C., Bishnu
K. C., Bhojraj Pathak, Salina Kadal, Milan Sapkota, Dipak Aryal, Kalpana Gyawali, Lalit K. Yadav, Sudeep
Khadka, Bhuban Timalsina, Rajaram Aryal, Naval K. Yadav, Bed P. Bhandari, Bhim B. Thapa, Ram
B. Khadka, Shivram Dhungana, Muna Neupane, Sundar Rai, Birendra K. Yadav, Nabraj Giri, Laxman
Chaudhary, Dharma C. Shakya, Krishna B. K. C., Nabina Sapkota, Anita Gautam, Madur Dahal, Sunil
Dhungana, Manju Badu.
Administraon, Accounts and Planning Team: Badri K. Sankhdev, Gopal Gautam, Jagdishwor
Sedhain, Mukunda R. Joshi, Keshab Prasai, Bikash Basnet, Bishnu P. Dhakal, Gopikrishna Gnawali,
Renuka Shrestha, Kalpana Raut, Laxman P. Aryal, Chudamani Bhandari, Mohan K. Khadka, Ramkumar
Pudasaini, Bijay Sharma, Devendra Acharya, Sabitra Ghimire.
Support Sta: Shyam Sangachhe, Purna B. Karki, Krishna B. Tamang, Om Timilsina, Hari Pokharel,
Padam Tamang, Ramesh Khadgi, Arun Karki, Kumar Gurung, Ram Lama, Mohan Shrestha, Prabhuram
Thapa Magar, Rameshwor Ranabhat, Nar B. Rai, Dhan B. Thing, Nanda B. Rai, Talak B. Mahat, Shree
K. Khadka, Govinda Suwal, Jeetlal Suwal, Uday B. Shrestha, Laxmi Nepal, Bishal Thapa, Rekha Thapa,
Premkumari Ranabhat, Tunakumari Giri, Ram G. Maharjan, Yuvraj Thapa, Laxman Kandel.
I am thankful to MFSC task force team—Man B. Khadka, Yam P. Pokharel, Sagar Rimal, Mohan Poudel,
and Bijaya R. Subedi—for their validaon checks during the report approval process. I acknowledge
the hardwork of Deepak K. Kharal, Yam P. Pokharel, Keshab R. Goutam, Buddi S. Poudel, Shiva Khanal,
Amul K. Acharya, and Kiran K. Pokharel in nalising this report.
I am thankful to Indufor Oy, the Finnish Forest Research Instute (Metla), Arbonaut, and Genesis
Consultancy for their support to the department in implemenng the project.
I am grateful to the Ministry of Forests and Soil Conservaon; Department of Forests; Department
of Soil Conservaon and Watershed Management; Department of Naonal Parks and Wildlife
Conservaon; Department of Plant Resources; Regional Forest Directorates; District Forest Oces;
District Soil Conservaon Oces; District Plant Resource Oces; Oces of Naonal Parks, Wildlife
Reserves, and Conservaon Area; Nepal Foresters' Associaon; oce bearers and members of
Community Forest User Groups, Leasehold Forest User Groups, Collaborave Forest and Buer Zone
Community Forest User Groups, and others for their contribuons.
Prakash Mathema
Director General
Department of Forest Research and Survey
STATE OF NEPAL’S FORESTS v
BRDF Bidirectional Reectance Distribution Function
CBS Central Bureau of Statistics
CCSP Concentric Circular Sample Plot
CDR Central Development Region
cm Centimetre
DBH Diameter at Breast Height (1.3 m)
DEM Digital Elevation Model
DFRS Department of Forest Research and Survey
DHM Department of Hydrology and Meteorology
DoF Department of Forests
DoS Department of Survey
EDR Eastern Development Region
FAO Food and Agriculture Organization of the United Nations
FRA Forest Resource Assessment
FWDR Far-Western Development Region
GIS Geographic Information System
GOFC-GOLD Global Observation of Forest and Land Cover Dynamics
ha Hectare
HH High Himal
HM High Mountains
IPCC Intergovernmental Panel on Climate Change
LMH Lower Mixed Hardwood
LRMP Land Resources Mapping Project
m3/ha Cubic metre per hectare
MFSC Ministry of Forests and Soil Conservation
mm Millimetre
MM Middle Mountains
MPFS Master Plan for the Forestry Sector
MSS Multi-Spectral Scanner
MWDR Mid-Western Development Region
NFI National Forest Inventory
NTFPs Non-Timber Forest Products
ACRONYMS AND ABBREVIATIONS
STATE OF NEPAL’S FORESTS
vi
OC Organic Carbon
OL Other Land
OWL Other Wooded Land
PA Protected Area
PSP Permanent Sample Plot
RMSE Root Mean Square Error
RS Remote Sensing
SD Standard Deviation
SOC Soil Organic Carbon
t/ha tonne per hectare
TMH Terai Mixed Hardwood
UMH Upper Mixed Hardwood
USAID United States Agency for International Development
WDR Western Development Region
STATE OF NEPAL’S FORESTS vii
GLOSSARY
Above-ground
biomass
It refers to the biomass of trees (≥10 cm DBH) above the soil; it
includes dead wood but not stumps.
Below-ground
biomass
It refers to the biomass of trees (≥10 cm DBH) contained within live
roots and stumps.
Biomass The biological material derived from living or recently living
organisms. It includes both the above- and below-ground biomass
of trees and saplings.
Bulk density Soil mass per unit volume, expressed in g/cm3.
Carbon stock Carbon content in above-ground and below-ground biomass, and
soil.
Cull tree A malformed tree that does not meet, and cannot be expected to
meet regional merchantability standards.
Debris Fallen dead trees and the remains of large branches (<10 cm
diameter) on the forest oor.
Forest An area of land at least 0.5 ha and a minimum width/length of 20 m
with a tree crown cover of more than 10% and tree heights of 5 m
at maturity.
Forest type The species which has more than 60% basal area is dened as that
forest type.
Growing stock The sum of all trees by number or volume or biomass growing in a
unit area.
High-quality sound
tree
A live tree which will yield at least a 6 m long saw log.
Land cover The bio-physical material covering the surface of the earth.
Lier Dead plant materials such as leaves, bark, needles, and twigs that
have fallen to the ground.
Lower Mixed Hard-
wood (LMH)
Mixed hardwood forests generally found between 1,000–2,000 m
altude.
Non-reachability A plot is regarded as non-reachable if the slope within the plot is
more than 45 degrees (100 %).
Other Land All land that is not classied as Forest or Other Wooded Land.
STATE OF NEPAL’S FORESTS
viii
Other Wooded Land
(OWL)
The land not classied as forest spanning more than 0.5 ha, having
at least 20 m width and a tree canopy cover of trees between 5%
and 10%.
or
The canopy cover of trees less than 5% but the combined cover of
shrubs, bushes and trees more than 10%; includes area of shrubs
and bushes where no trees are present.
Shrub An area occupied by woody perennial plants, generally 0.5–5.0 m
height at maturity, and oen without denite stems or crowns.
Sound tree A live tree not qualied as class 1 but able to produce at least one
3 m saw log or two 1.8 m saw logs.
Terai Mixed Hard-
wood (TMH)
A low-altude, broadleaved forest in which no species constutes
60% of the total basal area.
Upper Mixed Hard-
wood (UMH)
Mixed hardwood forests generally found above 2,000 m.
Wall-to-wall mapping Mapping that covers an enre area.
STATE OF NEPAL’S FORESTS ix
MAIN RESULTS
Forest Cover
1. Forest occupies a total of 5.96 million ha which is 40.36% of the total area of the
country. Other Wooded Land (OWL) covers 0.65 million ha (4.38%). Forest and OWL
together represent 44.74% of the total area of the country.
2. Out of the total area of Forest, 82.68% (4.93 million ha) lies outside Protected Areas
and 17.32% (1.03 million ha) inside Protected Areas. Within the Protected Areas, Core
Areas and Buer Zone contain 0.79 and 0.24 million ha of Forest, respecvely.
3. Out of the total area of Forest, 37.80% lies in Middle Mountains physiographic region,
32.25% in High Mountains and High Himal, 23.04% in Churia and 6.90% in Terai. In
case of OWL, Terai, Churia, Middle Mountains, and High Mountains and High Himal
physiographic regions share 1.47%, 3.50%, 9.61% and 85.42%, respecvely.
Growing Stock
4. The total number of stems with Diameter at Breast Height (DBH) ≥10 cm esmated in
the Forest of Nepal is 2,563.27 million (429.93/ha). The esmated total stem volume
is 982.33 million m3 (164.76 m3/ha).
5. High Mountains and High Himal physiographic regions together has the highest stem
volume per hectare (225.24 m3/ha) whereas Middle Mountains has the lowest stem
volume per hectare (124.26 m3/ha). Terai and Churia regions have 161.66 m3/ha and
147.49 m3/ha, respecvely.
6. The total above-ground air-dried biomass in the Forest of Nepal is 1,159.65 million
tonnes (194.51 t/ha).
Carbon Stock
7. The total carbon stock in Nepal’s Forest has been esmated as 1,054.97 million tonnes
(176.95 t/ha). Out of this total, tree component (live, dead standing, dead wood and
below-ground biomass), forest soils, and lier and debris constute 61.53%, 37.80 %,
and 0.67%, respecvely.
Biodiversity
8. A total of 443 tree species belonging to 239 genera and 99 families were idened in
the sample plots. The number of tree species idened in the sample plots of Middle
Mountains, Churia, High Mountains along with High Himal and Terai regions were 326,
281, 275 and 164, respecvely.
Forest Disturbance
9. Among all physiographic regions, Churia was observed to have the highest occurrence
of forest disturbance parcularly grazing, forest re, landslide and bush cung.
STATE OF NEPAL’S FORESTS
x
k|d'v glthfx?
jg If]q
!= g]kfndf jgn] %( nfv ^@ xhf/ x]S6/ e"–efu cf]u6]sf] 5, h'g g]kfnsf] s'n If]qkmnsf]
$)=#^Ü x'g cfpF5 . cGo sfi7 tyf a'6\ofg If]q (Other Wooded Land) ^ nfv $* xhf/
x]S6/ -$=#*Ü_ /x]sf] 5 . g]kfnsf] s'n If]qkmndWo] jg If]q / cGo sfi7 tyf a'6\ofg If]q
b'a}n] u/L $$=&$Ü e"–efu cf]u6]sf] 5 .
@= s'n jg If]qkmndWo] :f+/lIft If]q eGbf aflx/sf] efudf $( nfv @( xhf/ x]S6/ -*@=^* Ü_
/ :f+/lIft If]qdf !) nfv ## xhf/ x]S6/ -!&=#@Ü_ jg /x]sf] kfOPsf] 5 . :f+/lIft If]qsf]
leqL efu -Core Area_ df & nfv (# xhf/ x]S6/ / dWojtL{ If]qdf @ nfv $) xhf/
x]S6/ jg /x]sf] kfOPsf] 5 .
#= s'n jg If]qkmnsf] #&=*)Ü dWokxf8L ef}uf]lns If]qdf, #@=@%Ü pRrkxf8L tyf pRrlxdfnL
If]qdf, @#=)$Ü r'/] If]qdf / ^=()Ü t/fO{ If]qdf cjl:yt 5 . o;}u/L cGo sfi7 tyf a'6\ofg
If]q dWo] t/fO{, r'/], dWokxf8L / pRrkxf8L tyf pRrlxdfnL ef}uf]lns If]qx?df qmdzM
!=$&Ü, #=%)Ü, (=^!Ü, / *%=$@Ü /x]sf] 5 .
?vsf] df}Hbft
$= g]kfnsf] jg If]qdf !) ;]=ld= eGbf al9 Jof; ePsf ?vsf] ;+Vof @ ca{ %^ s/f]8 ## nfv
-$@(=(# k|lt x]S6/_ / s'n sf08 cfotg (* s/f]8 @# nfv #@ xhf/ 3g ld6/ -!^$=&^
3g ld6/ k|lt x]S6/_ cg'dfg ul/Psf] 5 .
%= sf08sf] cf};t cfotg (mean stem volume) ;a}eGbf al9 -@@%=@$ 3g ld6/ k|lt x]S6/_
pRrkxf8L tyf pRrlxdfnL ef}uf]lns If]qdf kfOPsf] 5 eg] dWokxf8L If]qdf ;a}eGbf sd
-!@$=@^ 3g ld6/ k|lt x]S6/_ kfOPsf] 5 . t/fO{ / r'/] ef}uf]lns If]qx?df qmdzM !^!=^^
/ !$&=$( 3g ld6/ k|lt x]S6/ sf08 cfotg /x]sf] kfOof] .
^= g]kfnsf] jg If]qdf ?vsf] s'n h}ljs lk08 -air-dried biomass_ ! ca{ !% s/f]8 (& nfv
6g -!($=%! 6g k|lt x]S6/_ /x]sf] 5 .
sfj{gsf] ;+lrlt
&= g]kfnsf] jg If]qdf s'n sfa{g ;+lrlt ! ca{ % s/f]8 %) nfv 6g -!&^=(% 6g k|lt x]S6/_
/x]sf] cg'dfg ul/Psf] 5 h:fdWo] ^!=%#Ü efu ?vdf -hLljt, ;'v8 v8f, 9nfk8f / ;tx
d'lgsf] efu ;d]t_, #&=*)Ü df6f]df / )=^&Ü kftklt+u/ (lier and debris) df /x]sf]
kfOof] .
h}ljs ljljwtf
*= dfkg ul/Psf jg If]qdf (( kl/jf/ -families_ cGtu{t @#( hflt -genera_ sf s'n $$#
Jf6f ?v k|hflt -species_ klxrfg ul/Psf 5g\ . dWokxf8L, r'/], pRrkxf8L tyf pRrlxdfnL
/ t/fO{ ef}uf]lns If]qx?df qmdzM #@^, @*!, @&% / !^$ j6f ?v k|hfltx? gd'gf Kn6df
e]l6Psf lyP .
k|lts"n k|efjx?
(= cGo ef}uf]lns If]qx?sf] t'ngfdf r'/] ef}uf]lns If]qsf] jgdf ;a}eGbf al9 k|lts"n k|efjx?
ePsf] kfOof] h;dWo] rl/r/g, jg 89]nf], klx/f] / emf8L s6fgL k|d'v x'g\ .
STATE OF NEPAL’S FORESTS xi
EXECUTIVE SUMMARY
The Department of Forest Research and Survey (DFRS) implemented Forest Resource Assessment
(FRA) Nepal Project (2010–2014) with nancial and technical assistance from the Government
of Finland. The project was designed to carry out naonal-level forest resource assessment,
with an overall objecve of providing comprehensive and up-to-date naonal-level forest
resource informaon to support forest policy formulaon, forestry sector decision-making and
internaonal reporng. The report presents informaon primarily on forest cover, growing stock,
biomass, carbon stock, biodiversity and forest disturbances.
Forest cover maps were prepared and classied as Forest, Other Wooded Land (OWL) and Other
Land (non-forest) using RapidEye MSS satellite imagery, secondary images (Google Earth images,
Landsat), ancillary maps (LRMP and topographical maps) and the FRA Nepal eld inventory data.
Images were classied by applying an automated method of object-based image analysis method
on segmented images using eCognion soware. In order to conduct the forest inventory, a two-
phased straed systemac cluster sampling design was adopted. Five physiographic regions—
Terai, Churia, Middle Mountains, High Mountains and High Himal—were considered as strata. At
the rst phase, a total of 9,230 clusters (55,358 plots) were laid out systemacally at the nodes of
4 km × 4 km square grids placed across the country. These plots were interpreted by using high-
resoluon RapidEye imagery and Google Earth. At the second phase, a total of 2,544 sample
plots (Forest: 1,553; OWL: 105; OL: 886) were measured in the eld. Each sample plot consisted
of four concentric circular sample plots (CCSP) of dierent radii, four vegetaon sub-plots, four
shrubs and seedlings sub-plots, and four soil pits.
As per this assessment, Forest covers 5.96 million ha (40.36%), Other Wooded Land covers 0.65
million ha (4.38%) and Other Land covers 8.16 million ha (55.26%). Forest and OWL together
comprise 44.74% of the total area of the country. Out of the total forest area of Nepal, 37.80%
lies in Middle Mountains region, 32.25% in High Mountains and High Himal, 23.04% in Churia
and 6.90 in the Terai. The Mid-Western Development Region has the highest (26.68 %) forest
cover of Nepal, whereas Far-Western Development Region has the lowest (16.94 %) of the total
forest area. Out of the total forest area of the country, 4.93 million ha (82.68%) lies outside
Protected Areas and 1.03 million ha (17.32%) inside Protected Areas.
The esmated total number of stems with Diameter at Breast Height (DBH) ≥10 cm is 3,112.28
million, of which 2,563.27 million (429.93/ha) is in Forest. The total esmated stem volume with
DBH ≥10 cm is 1,063.56 million m3; out of which 982.33 million m3 (164.76 m3/ha) is in Forest,
4.58 million m3 (7.91 m3/ha) in OWL and 76.65 million m3 (14.49 m3/ha) in Other Land. High
Mountains and High Himal physiographic regions together has the highest stem volume per
hectare (225.24 m3/ha) whereas Middle Mountains has the lowest (124.26 m3/ha) in Forest. Terai
and Churia regions have 161.66 m3/ha and 147.49 m3/ha, respecvely. Of the total stem volume
in Forest, Shorea robusta has the highest stem volume (31.76 m3/ha) followed by Quercus spp.
(24.39 m3/ha) and Pinus roxburghii (11.62 m3/ha). The average above-ground air-dried biomass
in Forest is 194.51 t/ha. The assessment showed an increase in the number of stems from 408/ha
in NFI 1987–98 to 430/ha in FRA 2010–2014. However, the mean stem volume per hectare was
found to be less in FRA 2010–2014 (164.76 m3/ha) than in NFI 1987–98 (178 m3/ha).
STATE OF NEPAL’S FORESTS
xii
The total carbon stock in Nepal is esmated to be 1,157.37 million tonnes, out of which Forest,
OWL and OL constute 1,054.97 million tonnes (176.95 t/ha), 60.92 million tonnes (105.24 t/ha)
and 41.48 million tonnes (7.84 t/ha), respecvely. Out of the total forest carbon stock, tree, soil
and lier/debris components contribute 61.53%, 37.80 %, and 0.67% of carbon, respecvely.
Altogether, 443 tree species belonging to 239 genera and 99 families were recorded in the
sample plots. The highest number of taxa was found in Middle Mountains region (326 species)
and the lowest in Terai region (164 species). Nearly two-thirds of the total forest area in the
country was aected by grazing. Tree cung, bush cung, lathra cung, lopping and forest re
were also common.
The results reported here provide an important insight into the forests of Nepal which will
help the Government and concerned stakeholders in decision-making towards sustainable
forest management. Sample plots selected for this assessment were permanently established
for regular monitoring. This, together with the instuonal capacity strengthened during the
project, will help to conduct periodic forest resource assessment in the future.
STATE OF NEPAL’S FORESTS xiii
;f/f+z
jg cg';Gwfg tyf ;j]{If0f ljefun] ;+rfng u/]sf] jg ;|f]t ;j]{If0f cfof]hgf g]kfn ;/sf/ /
lkmgNof08 ;/sf/sf] låkIfLo ;xof]udf ;+rflnt cfof]hgf xf] . of] cfof]hgfsf] d'Vo p2]Zo /fli6«o
:t/df g]kfnsf] jg ;|f]tsf] ;j]{If0f u/L pko'St gLlt /0fgLlt th'{df ug{ ;xof]u k'¥ofpg'sf] ;fy}
jg ;DaGwL lj:t[t / cfjlws tYof+s tyf ;"rgfx? k|bfg ug'{ /x]sf] lyof] . o; /fli6«o k|ltj]bgdf
d'Votof jg If]q, jgsf] df}Hbft, h}ljslk08, sfa{g ;+lrlt, h}ljs ljljwtf / jgdf x'g] k|lts"n
k|efjx? ;DaGwL glthfx? k|:t't ul/Psf] 5 .
g]kfnsf] ;Dk"0f{ e"–efunfO{ e"–pku|x lrqx?sf] ;fy} cGo gS;fx? cWoog u/L lkmN8 sfo{ ;d]tsf]
cfwf/df jg (Forest), a'6\ofg (Other Wooded Land) / cGo If]q (Other Land) u/L tLg efudf
jlu{s/0f u/L gS;f+sg ul/Psf] lyof] . jg;|f]t ;j{]If0fsf] nflu klxnf] r/0fdf kfFr j6} ef}uf]lns
If]qx?df rf/ ls= ld= sf] juf{sf/ lu|8 agfO{ (,@#) 7fpFdf hDDff %%,#%* Kn6x? /fvL cWoog
ul/Psf] lyof] eg] bf];|f] r/0fdf jgdf !,%%#, a'6\ofgdf !)% / cGo If]qdf **^ u/L hDdf @,%$$
gd'gf Kn6x? lkmN8df uO{ gfFkhfFr ul/Psf] lyof] . k|To]s gd'gf Kn6df rf/ j[QLo 3]/fx? agfO{ ?vsf]
;fO{h cg';f/ dfkg ul/Psf] lyof] . ;f]lx Kn6leq c? ;fgf] ;fOhsf ;a–Kn6x? agfO{ 3fF;÷emf/,
a'6\ofg÷la?jf÷nfy|fsf] tYof+s ;+sng ul/Psf] lyof] . o;sf] cltl/Qm df6f]df /x]sf] sfa{g cf+sng
ug{ Kn6sf] aflx/kl§ rf/j6f s'gfaf6 df6f]sf] gd'gf ;+sng ;d]t ul/Psf] lyof] .
g]kfndf jgn] sl/a %( nfv ^@ xhf/ x]S6/ e"–efu cf]u6]sf] 5 h'g g]kfnsf] s'n If]qkmnsf] $)=#^Ü
x'g cfpF5 . a'6\ofg If]q ^ nfv $* xhf/ x]S6/ -$=#*Ü_ /x]sf] 5 . o;/L g]kfnsf] s'n If]qkmn dWo]
jg tyf a'6\ofg If]qn]] u/L hDdf $$=&$Ü e"–efu cf]u6]sf] 5 . s'n jg If]qsf] #&=*)Ü dWokxf8L
ef}uf]lns If]qdf, #@=@%Ü pRrkxf8L tyf pRrlxdfnL If]qdf, @#=)$Ü r'/] If]qdf / ^=()Ü t/fO{
If]qdf cjl:yt 5 . s'n jg If]q dWo] :f+/lIft If]qdf !) nfv ## xhf/ x]S6/ -!&=#@Ü_ / :f+/lIft If]q
eGbf aflx/ $( nfv @( xhf/ x]S6/ -*@=^*Ü_ jg /x]sf] 5 .
g]kfndf !) ;]=ld= eGbf al9 Jof; ePsf ?vx?sf] s'n ;+Vof # ca{ !! s/f]8 @# nfv 5 h;dWo]
jg If]qdf @ ca{ %^ s/f]8 ## nfv -$@(=(# k|lt x]S6/_ 5g\ . g]kfndf sf08sf] s'n cfotg sl/a
! ca{ ^ s/f]8 #^ nfv 3g ld6/ /x]sf] 5 h;dWo] sl/a (* s/f]8 @# nfv 3g ld6/ -!^$=&^
3g ld6/ k|lt x]S6/_ jg If]qdf kfOof] . pRrkxf8L tyf pRrlxdfnL ef}uf]lns If]qsf] jgdf ;a}eGbf
al9 cfotg -@@%=@$ 3g ld6/ k|lt x]S6/_ / dWokxf8L If]qdf ;a}eGbf sd -!@$=@^ 3g ld6/
k|lt x]S6/_ kfOPsf] 5 . t/fO{ / r'/] If]qdf qmdzM !^!=^^ / !$&=$( 3g ld6/ k|lt x]S6/ cfotg
kfOof] . ?v k|hfltsf] cfwf/df x]bf{ jgIf]qsf] cf};t cfotg dWo] ;fn k|hfltsf] ;a}eGbf al9 -#!=&^
3g ld6/ k|lt x]S6/_ kfOPsf] 5 eg] To;kl5 qmdzM v;|' -@$=#( 3g ld6/ k|lt x]S6/_ / vf]6]
;Nnf -!!=^@ 3g ld6/ k|lt x]S6/_ k|hfltsf] /x]sf] kfOof] . jg If]qdf ?vsf] hldg dflysf] cf};t
h}ljslk08 !($=%! 6g k|lt x]S6/ /x]sf] kfOof] . sl/a aL; jif{ cufl8 ul/Psf] /fli6«o jg ;j]{If0fsf]
STATE OF NEPAL’S FORESTS
xiv
t'ngfdf xfn k|lt x]S6/ ?vsf] ;+Vofdf s]lx j[l4 ePsf] -$)* af6 $#)_ / sf08sf] cfotg df}Hbftdf
s]lx lu/fj6 -!&* af6 !^% 3g ld6/_ cfPsf] kfOPsf] 5 .
g]kfndf s'n sfa{g ;+lrlt sl/a ! ca{ !% s/f]8 &$ nfv 6g ePsf] cg'dfg ul/Psf] 5 h;dWo]
jgdf sl/a ! ca{ % s/f]8 %) nfv -cf};tM !&^=(% 6g k|lt x]S6/_, a'6\ofgdf sl/a ^ s/f]8 ( nfv
-cf};tM !)%=@$ 6g k|lt x]S6/_ / cGo If]qdf sl/a $ s/f]8 !% nfv 6g -cf};tM &=*$ 6g k|lt
x]S6/_ /x]sf] kfOof] . jg If]qsf] s'n sfa{g ;+lrlt dWo] ^!=%#Ü ?vdf, #&=*)Ü df6f]df / )=^&Ü
kftklt+u/ (lier and debris_ df /x]sf] kfOof] .
o; ;j{]If0fsf] qmddf dfkg ul/Psf gd'gf Kn6x?df s'n $$# k|sf/sf] ?vsf] k|hfltx? -@#( hflt /
(( Kfl/jf/_ kfOPsf] 5 . ;a}eGbf al9 ?vsf k|hfltx? dWokxf8L If]qdf -#@^ k|hflt_ / ;a}eGbf sd
t/fO If]qdf -!^$ k|hflt_ kfOPsf 5g\ . g]kfnsf] sl/a b'O{–ltxfO jg If]qdf rl/r/gsf] k|efj /x]sf]
kfOof] . To;}u/L cGo k|efjx?df ?v, nfy|f / la?jf s6fgL tyf jg 89]nf] pNn]Vo dfqfdf kfOof] .
o; ;j]{If0faf6 g]kfnsf] jg If]qsf] ljljw ljifox?df hfgsf/L k|fKt ePsf] / o;/L k|fKt glthfx?
g]kfn ;/sf/ / cGo ;/f]sf/jfnfx?nfO{ jg ;|f]tsf] lbuf] Joj:yfkgdf cfjZos lg0f{o lng ;xof]u
k'Ug] ck]Iff ul/Psf] 5 . ;j]{If0fsf] qmddf dfkg ul/Psf Kn6x?nfO{ :yfoL gd'gf Kn6sf] ?kdf :yfkgf
ul/Psf] 5 . o;sf ;fy} jg ;|f]t ;j]{If0fsf] nflu cfjZos ;+:yfut Ifdtfdf ;d]t clej[l4 ePsf]
5, o;af6 eljiodf jg ;|f]t ;j]{If0f sfo{nfO{ cfjlws ?kdf ;+rfng u/L jg tyf sfa{g ;DaGwL
;"rgf tyf tYof+sx? cWofjlws ug{df ;xof]u k'Ug] ljZjf; lnOPsf] 5 .
STATE OF NEPAL’S FORESTS xv
Contents
MESSAGE i
FOREWORD ii
ACKNOWLEDGEMENTS iii
ACRONYMS AND ABBREVIATIONS v
GLOSSARY vii
MAIN RESULTS ix
EXECUTIVE SUMMARY xi
CONTENTS xv
LIST OF FIGURES xvi
LIST OF TABLES xvii
1 INTRODUCTION 1
1.1 Background 1
1.2 Physiographic Seng 1
1.3 Vegetaon 4
1.4 Forestry Sector Policies 5
1.5 Populaon 6
2 PREVIOUS FOREST RESOURCE ASSESSMENTS 8
2.1 Forest Resources Survey 8
2.2 Land Resources Mapping Project 8
2.3 Forest Resources and Deforestaon in the Terai 8
2.4 Naonal Forest Inventory 9
2.5 Forest Cover Change Analysis of the Terai Districts 9
3 METHODOLOGY 10
3.1 Land Cover Mapping 10
3.2 Forest Resource Inventory 12
3.3 Sample Plot Design 15
3.4 Forest Soils 18
3.5 Forest Biodiversity 20
3.6 Forest Disturbance 20
4 LIMITATIONS 22
4.1 Land Cover Mapping 22
4.2 Forest Resource Inventory 24
4.3 Assessment of Forest Soils 24
5 RESULTS 25
5.1 Land Cover 25
5.2 Forest Inventory 32
5.3 Carbon Stock 40
5.4 Biodiversity 42
5.5 Forest Disturbances 43
6 WAY FORWARD 45
References 46
Annex 49
STATE OF NEPAL’S FORESTS
xvi
LIST OF FIGURES
Figure 1: Physiographic regions of Nepal 2
Figure 2: Annual total precipitaon (1970–2009) 3
Figure 3: River basin and drainage of Nepal4
Figure 4: Populaon density distribuon in Nepal 7
Figure 5: RapidEye image les and ground control points used for mapping 11
Figure 6: Layout of sample plot /cluster 13
Figure 7: Distribuon of sample plots15
Figure 8: Layout of concentric circular permanent sample plots and sub-plots16
Figure 9: Collecon of composite samples of lier, debris and soil from a plot 19
Figure 10: Defoliated Acacia catechu forest in Mid-Western Nepal 23
Figure 11: Undened (fuzzy) boundaries of forest areas 23
Figure 12: Land cover map of Nepal 26
Figure 13: Proporon of land cover in each physiographic region 27
Figure 14: Proporon of forest cover by development and physiographic region 28
Figure 15: Proporon of forest types at naonal level 29
Figure 16: Forest type map of Nepal30
Figure 17: Number of stems by DBH class33
Figure 18: Number of stems per hectare in Forest by common species 34
Figure 19: Number of stems per hectare in Forest by quality class and
physiographic region34
Figure 20: Proporonal volumes by DBH class and physiographic region 36
Figure 21: Variability of SOC with the elevaon in dierent physiographic
regions41
Figure 22: Number of families, genera and species of tree by physiographic
region 42
Figure 23: Occurrence of common tree species in Forest sample plots 43
Figure 24: Occurrence of forest disturbances 43
Figure 25: Proporonal occurrence of forest disturbances by physiographic
region 44
STATE OF NEPAL’S FORESTS xvii
LIST OF TABLES
Table 1: Vegetaon types and eco-regions in Nepal 5
Table 2: Distribuon of ecosystems by physiographic region 5
Table 3: Populaon characteriscs of Nepal by physiographic region6
Table 4: Distribuon of the rst phase sample plots by physiographic region 14
Table 5: Distribuon of permanent sample plots and clusters14
Table 6: Size and area of CCSP of dierent radii with DBH limits15
Table 7: Land cover area by physiographic region25
Table 8: Land cover by Development Region 27
Table 9: Forest cover by physiographic and Development Region 28
Table 10: Forest cover inside and outside Protected Areas by physiographic
region 28
Table 11: Error matrix of land cover map using independent ground
vericaon samples 31
Table 12: Forest cover found by dierent assessments31
Table 13: Forest cover by Development Region 32
Table 14: Number of seedlings and saplings per ha in Forest by physiographic
region32
Table 15: Number of stems (≥10 cm DBH) by land cover class33
Table 16: Number of stems/ha in Forest by DBH class and physiographic
region33
Table 17: Stem distribuon by quality class in Forest (million) 35
Table 18: Basal area by land cover class35
Table 19: Basal area (m2/ha) by DBH class35
Table 20: Total stem volume per ha (≥10 cm DBH) by land cover class 36
Table 21: Stem volume (m3/ha) in Forest by DBH class36
Table 22: Stem volume in Forest by species 37
Table 23: Stem volume (m3/ha) in Forest by quality class and physiographic
region37
Table 24: Stem volume in Forest by quality class and physiographic region38
Table 25: Tree component-wise total biomass by land cover class38
Table 26: Above-ground air- and oven-dried biomass of tree component 38
Table 27: Above-ground air-dried biomass of tree component by DBH class39
Table 28: Total above-ground air-dried biomass in Forest by species 39
Table 29: Standard errors and condence limits in Forest for physiographic
region 40
Table 30: Number of stems per hectare by DBH class in two inventories 40
Table 31: Proporon of stem volume available in two inventories by common
species 40
Table 32: Carbon stock (t/ha) in Nepal41
Table 33: Soil organic carbon, lier and debris and tree component carbon
stock in Forest by physiographic region 41
STATE OF NEPAL’S FORESTS 1
1.1 Background
Department of Forest Research and Survey (DFRS) under the Ministry of Forests and Soil
Conservaon (MFSC) executed Forest Resource Assessment (FRA) Nepal Project (2010–2014),
under the bilateral agreement between the Government of Nepal and the Government of
Finland. The project was designed to carry out naonal level forest resource assessment for
providing comprehensive and up-to-date naonal-level forest resource informaon to support
forest policy formulaon, naonal-level forestry sector decision-making and internaonal
reporng.
This report presents the ndings of FRA 2010–2014. It summarises the results of the forest
resource assessment of physiographic regions of Nepal presented in the region-specic reports
viz. “Terai Forests of Nepal”, “Churia Forests of Nepal”, “Middle Mountains Forests of Nepal”, and
“High Mountains and High Himal Forests of Nepal”. This report presents informaon primarily on
land cover, forest cover, growing stock, structural composion of tree species, biomass, carbon
stock and forest disturbance. It aempts to address the demand for forest resource informaon
at naonal and internaonal levels.
The forest resource assessment made use of high-resoluon satellite imagery, precise
measurement devices, advanced computer systems, and trained human resources to obtain
reliable output.
1.2 Physiographic Seng
1.2.1 Geography
Nepal is located between 260 22’ N and 300 27’ N latude and 800 04’ E and 880 12’ E longitude.
There are ve physiographic regions in Nepal (Figure 1) based on geology and geomorphology
(LRMP, 1986).
Terai physiographic region of Nepal occupies 13.7% of the total land area of the country. In terms
of geomorphology, it consists of gently sloping recent and post-Pleistocene alluvial deposits,
which form a piedmont plain south of the Himalayas. Its elevaon varies from 63 m to 330 m
above mean sea level (LRMP, 1986).
Churia region is the youngest mountain range in the Himalayas. Just north of the Terai, it runs the
enre length of southern Nepal, from east to west, skirng the southern anks of the Himalayas.
The region occupies about 12.8 % of the total land area of the country, and covers parts of 36
districts of Nepal (DoS, 2001). The elevaon of Churia varies from 93 to 1,955 m above mean
sea level.
1
INTRODUCTION
STATE OF NEPAL’S FORESTS
2
Middle Mountains region lies north of Churia along the southern anks of the Himalayas. The
region occupies 29.2% of the total land area of the country and covers parts of 55 districts. The
elevaon of Middle Mountains region varies from 110 m in the lower river valleys to 3,300 m
above mean sea level.
High Mountains region occupies 20.4% of the total land area of the country and covers parts of
40 districts. The elevaon of High Mountains region varies from 543 m in the river valley oors
to 4,951 m above mean sea level. The region is characterised by the rugged landscape and very
steep slopes.
High Himal region which includes the highest Himalayan massifs occupies about 23.9% of the
total land area of the country, and covers parts of 25 districts. The region's elevaon ranges from
1,960 m to 8,848 m above mean sea level.
Figure 1: Physiographic regions of Nepal
1.2.2 Soils
Terai region consists of recent and post-Pleistocene alluvial deposits that form a piedmont plain
(Carson et al., 1986). The lower Churia is largely composed of very ne-grained sediments such
as variegated mudstone, siltstone and shale with smaller amounts of ne-grained sandstone
(Upre, 1999). The middle Churia has thick beds of mul-storied sandstones alternang with
subordinate beds of mudstone. The upper Churia is characterised by very coarse sediments
such as loose boulder conglomerates. Dominant soil texture found in Middle Mountains region
ranges from fragmented sandy to loamly/boulderly, loamy, loamy skeletal as per the diverse land
forms. High Mountains soils are rocky mostly derived from phyllite, schist, gneiss and quartzite
of dierent ages. High Himal physiographic region is characterised by rocky soils originated from
gneiss, schist, limestone and shale of dierent ages (Pariyar, 2008).
1.2.3 Climate
The climate of Nepal greatly varies from north to south and east to west. In general, climac zones
in Nepal are categorised by temperature regimes based on altudinal ranges. These climac
zones are sub-tropical (<1,000 m elevaon), warm-temperate (1,000–2,000 m elevaon), cool-
temperate (2,000–3,000 m elevaon), alpine (3,000–4,000 m elevaon) and arcc (>4,500 m
High Himal
Churia
STATE OF NEPAL’S FORESTS 3
elevaon).
Terai region is located in sub-tropical climac zone characterised by hot and humid summers,
intense monsoon rain, and dry winters. The annual rainfall decreases gradually from the Eastern
to the Western Terai. The annual total rainfall in this region varies from 1,138 mm to 2,680 mm,
and the mean monthly precipitaon ranges from 8 mm to 535 mm.
The climate of Churia region ranges from sub-tropical to warm-temperate and is characterised
by hot and sub-humid summers, intense monsoon rain, and cold dry winters. The precipitaon
paern in Churia is variable, with the highest annual rainfall in the Eastern and Central
Development Regions. The total annual rainfall varies from a minimum of 1,138 mm to the
maximum of 2,671 mm.
In Middle Mountains, the climate ranges from sub-tropical, sub-humid in river valleys to warm-
temperate in valleys to cool-temperate in the high hills. Annual precipitaon varies from east to
west with the highest in the Western Development Region (1,898 mm).
The climate in High Mountains and High Himal regions ranges from warm-temperate in the valleys
to cool-temperate in the higher elevaons and arcc in the upper most elevaons. Precipitaon
in the region varies from east to west with the highest in the Central Development Region with
a total annual precipitaon of 2,185 mm (Figure 2). Trans-Himalayan areas receive very lile
precipitaon, and are also known as cold desert.
Figure 2: Annual total precipitaon (1970–2009)
1.2.4 Drainage
Terai region is drained by numerous rivers and rivulets (Figure 3). The largest, among them are
Koshi in the east, Gandaki in the centre, and Karnali and Mahakali in the west. These rivers
originate from the Himalayan Region and even beyond the Himalayas. As the rivers cross the hills
and Churia, they start deposing huge sediments along their banks in the Terai. The deposion
process creates mulple channels of the rivers. Every year during monsoon season, most of
the rivers are swollen up and cause ash oods in the Terai due to their shallow beds. One of
the biggest concerns is the tendency of minor and major rivers to change their courses due to
ooding events (Carson et al., 1986).
STATE OF NEPAL’S FORESTS
4
Figure 3: River basin and drainage of Nepal
Several large rivers originang in High Himal region cut the east-west Churia chain, while
smaller, ephemeral rivers ow only during the monsoon season. Water in the small rivers may
dry up totally outside the monsoon season, probably because the soil in the river beds is highly
permeable (Shrestha et al., 2008). Churia region is the origin of the third-grade rivers of Nepal.
These rivers are characterised by their smaller sizes and low to almost no ow during the dry
season.
In Middle Mountains region, rivers originang in the Lesser Himalaya and the Mahabharat Range
are called the second-grade rivers. They are fed by precipitaon as well as ground water recharge
(WECS, 2011). These rivers are perennial and are commonly characterised by wide seasonal
uctuaon in discharge. The major river systems in this region are Babai, West Rap, Tinau,
Bagma, Kamala, Kankai, and Mechi.
High Mountains and High Himal regions are the origin of rst-grade rivers of Nepal. These rivers
are snow-fed rivers, originate from the Himalayas, and ow across all the physiographic regions.
The rst-grade rivers that originate from the Himalayas are Mahakali, Karnali, Gandaki and Koshi.
1.3 Vegetaon
Nepal occupies about 0.1 percent of the global area but harbours over three percent of the
world's known ora. A total of 284 owering plants are endemic to Nepal. The number of known
species in Nepal is: 6,073 angiosperms; 26 gymnosperms; 534 pteridophytes; 1,150 bryophytes;
365 lichens; 1,822 fungi and 1,001 algae (GoN, 2014).
Phyto-geographically, Nepal is located in the Oriental Region (Polunin, 1964). According to the
Conservaon Science Programme WWF-US (1998), Nepal includes 12 eco-regions (TISC, 2002)
as given in Table 1.
STATE OF NEPAL’S FORESTS 5
Table 1: Vegetaon types and eco-regions in Nepal
Vegetaon type Eco-region Altude
Montane grasslands and
shrub lands
Trans-Himalayan alpine shrub/meadow
West Himalayan alpine shrub/meadow 3,7004,400 m
East Himalayan alpine shrub/meadow 4,0004,500 m
North-west Himalayan alpine shrub/meadow Above 4,000 m
Sub-alpine conifer forest Trans-Himalayan sub-alpine conifer forest
West Himalayan sub-alpine conifer forest 3,0004,000 m
East Himalayan sub-alpine conifer forest 3,0004,000 m
Temperate broadleaved
forest
West Himalayan broadleaved forest 1,5003,000 m
East Himalayan broadleaved forest 1,5003,000 m
Tropical forests/sub-tropical
conifer forest
Himalayan sub-tropical pine forest 1,0002,000 m
Sub-tropical broadleaved
forest
Himalayan sub-tropical broadleaved forest 5001,000 m
Grasslands, savannahs and
shrub lands
Tarai-Duar savannahs and grassland Below 500 m
Source: TISC (2002)
According to TISC (2002), Stainton (1972) idened 35 forest types in Nepal, largely based on
Champion (1936). These 35 types are oen categorised into 10 major groups (GoN, 2014): (i)
tropical, (ii) sub-tropical broadleaved, (iii) sub-tropical conifer, (iv) lower temperate broadleaved,
(v) lower temperate mixed broadleaved, (vi) upper temperate broadleaved, (vii) upper temperate
mixed broadleaved, (viii) temperate coniferous, (ix) sub-alpine and (x) alpine scrub. Similarly,
Biodiversity Prole Project (BPP) idened a total of 118 ecosystems in Nepal (BPP, 1995). Table
2 presents distribuon of ecosystems in dierent physiographic regions.
Table 2: Distribuon of ecosystems by physiographic region
Physiographic region Ecosystems
Number % Types
Terai 12 10.2 10 'forest' and two 'culvated'
Churia 14 11.9 13 'forest' and one culvated
'Dun'
Middle Mountains 53 44.9 52 'forest', and one 'culvated'
High Himal and High
Mountains
38 32.2 37 'forest' and one 'glacier/snow/
rock'
Others 1 0.8 'Water bodies; found in all zones,
except the Siwalik
Total 118 100
Source: BPP (1995)
1.4 Forestry Sector Policies
Nepal has well-dened policies and legal framework in the forestry sector. Some key policies and
legal instruments are: Naonalisaon of Private Forest Act, 1957; Naonal Parks and Wildlife
Conservaon Act, 1972; Naonal Forest Plan, 1976; Master Plan for Forestry Sector, 1989;
Forest Act, 1993; Revised Forestry Sector Policy, 2000; Leasehold Forestry Policy, 2002; Herbs
and NTFP Development Policy, 2004; Terai Arc Landscape Strategy, 2004–2014; Gender and
Social Inclusion Strategy in the Forestry Sector, 2004-19; Sacred Himalayan Landscape Strategy,
STATE OF NEPAL’S FORESTS
6
2006-16; Naonal Wetland Policy, 2012; Naonal Biodiversity Strategy and Acon Plan, 2014
and Forest Policy, 2015.
Forestry sector development in Nepal has been guided by periodic naonal plans and, unl
recently, by the Master Plan for Forestry Sector (MPFS, 1989; NPC, 2013). At present, Forest Policy,
2015 is the main policy document which guides sub-sectoral programmes relang to forests,
plant resources, wildlife, biodiversity, medicinal plants, and soil and watershed conservaon.
Periodic assessment and updang of informaon on forest resources of the country is also
included in the forest policy (GoN, 2015).
1.5 Populaon
Distribuon of populaon varies among the physiographic regions of Nepal and between rural
and urban regions of the country (CBS, 2011; CBS, 2012). Terai region is populated by 41.48%
of the naon’s populaon with a populaon density of 583.46 persons/km2. Populaon in the
Churia is 12.78% of the total populaon with a density of 191.56 persons/km2. In the Middle
Mountains region, the proporon of populaon is 38.17% with a density of 251.99 persons/
km2. Both the High Mountains and High Himal regions are sparsely populated with average
populaon densies of about 65.54 and 4.98 persons/km2, respecvely. High Mountains region
has about 6.94% of the total populaon of the country and High Himal has about 0.62% (Table
3 and Figure 4).
Table 3: Populaon characteriscs of Nepal by physiographic region
Physiographic
region Male Female Total
populaon %Household
Populaon
density/
km2
Terai 5,795,762 5,995,930 11,791,691 41.48 2,210,625 583.46
Churia 1,719,994 1,913,707 3,633,701 12.78 783,752 191.56
Middle
Mountains
5,128,216 5,722,844 10,851,070 38.17 2,450,369 251.99
High Mountains 946,610 1,025,637 1,972,247 6.94 399,008 65.54
High Himal 86,985 89,004 175,989 0.62 37,571 4.98
Total 13,677,567 14,747,122 28,424,698 5,881,325 192.48
Adapted from CBS (2011)
STATE OF NEPAL’S FORESTS 7
Figure 4: Population density distribution in Nepal (Adapted from CBS, 2011)
Figure 4: Populaon density distribuon in Nepal (Adapted from CBS, 2011)
STATE OF NEPAL’S FORESTS
8
Forests play a vital role in Nepal’s socio-economic development. In order to maximise forests’
contribuon to sustainable development, the forestry sector needs detailed and up-to-date
informaon on the status of the resource and informaon management systems. This informaon
is obtained by carrying out forest inventories periodically with the goal of recording the current
state and changes in the forests.
The rst naonal-level forest inventory was carried out in the 1960s. Since then, several forest
resource assessments have been carried out, each dierent in terms of its purpose, scale, scope,
design and technology used. The second naonal-forest inventory was carried out in the 1990s.
FRA Nepal (2010–2014) is the third and most comprehensive naonal-level forest resource
inventory.
2.1 Forest Resources Survey
The rst naonal-level forest inventory was conducted between 1963 and 1967 with support
from USAID (FRS, 1967). It covered the Terai, Inner Terai, and Churia Hills, as well as the southern
faces of the Mahabharat Range, but excluded most of the then Chitwan Division, which was
inventoried separately. The survey classied forests as either commercial or non-commercial,
and focused on collecng data from commercial forests, primarily on mber esmates of stock
and domesc consumpon of wood products. Methodologically, it used visual interpretaon
of aerial photographs taken in 1953–1958 and 1963–1964, mapping, and eld inventory. The
inventory provided the rst comprehensive assessment of commercial forests in Terai region as
well as those in the adjoining areas of the hilly region.
2.2 Land Resources Mapping Project
The Land Resources Mapping Project (LRMP) used a variety of methods for country-wide
assessment. It used aerial photographs taken between 1977 and 1979 with ground vericaon.
It focused on mapping of land use; producing forest cover maps; and assessing the type, size
and crown cover of forests. Both the high- and low-altude forests were mapped according to
crown cover (0–10%, 10–40%, 40–70%, and 70–100%), and scrubland (degraded forest) was
mapped separately. Each forest was dened on the basis of dominant species and its forest type
(coniferous, hardwood, or mixed). Land ulisaon maps at the scale of 1:50,000 were produced
by interpreng aerial photographs of the scale of 1:12,000.
2.3 Forest Resources and Deforestaon in the Terai
The then Forest Survey and Stascs Division, a division directly under MFSC, with support
from the Government of Finland, assessed forest resources and deforestaon in the Terai from
1978/79 to 1990/91 by using 1991 Landsat TM (28.5 m spaal resoluon) satellite imagery. It
covered all 20 districts in the Terai (3.4 million ha) excluding protected areas (PAs).
2
PREVIOUS FOREST RESOURCE
ASSESSMENTS
STATE OF NEPAL’S FORESTS 9
2.4 Naonal Forest Inventory
The second Naonal Forest Inventory (NFI) was conducted by DFRS with support from the
Government of Finland from 1987 to 1998. Using 1991 Landsat TM satellite images of the Terai
and aerial photographs of the hills taken in 1989–1992 (DFRS, 1999), it updated data on forest
cover and change, and produced forest stascs for all accessible forests, excluding those in
protected areas. The NFI categorised Middle Mountains region as Hilly Area. Three types of
inventories were carried out: using Landsat TM satellite imagery for 14 districts, a district-wise
forest inventory for 10 districts, and aerial photo interpretaon for 51 districts. District-wise
forest inventory data was used to esmate the forest and shrub cover in Middle Mountains
region. In the hills, photo-point sampling was used to esmate forest area as well as to carry out
forest inventory in the eld.
2.5 Forest Cover Change Analysis of the Terai Districts
In 2005, Department of Forests (DoF) conducted a study of forest cover change in the 20 Terai
districts by using Landsat 1990/91 and Landsat 2000/01 satellite images and classifying land into
six main categories (forest, degraded forest, grass land, barren land, water bodies, and other
land). Ground vericaon was conducted between September and November 2004. Although
this report focused mainly on Terai forests, it also included certain parts of Churia and Middle
Mountains forests.
STATE OF NEPAL’S FORESTS
10
3
METHODOLOGY
FRA Nepal implemented mul-source forest resources inventory by using high-resoluon
satellite imagery, eld inventory as well as other exisng data sources such as digital elevaon
model and naonal topographic maps. Categorisaon of land cover followed in FRA Nepal is
based on current internaonal pracces of FAO which is also adopted by IPCC for GHG emission
esmaon and reporng. The inventory design was largely based on the principle adopted for
NFI (1999) developed by Kleinn (1994). The design was tested in the eld and subsequently
revised to improve its funconality. Two-phase systemac cluster sampling was adopted for eld
measurement.
3.1 Land Cover Mapping
Land cover maps were prepared by using RapidEye MSS satellite imagery (Level 1b, 48 scenes
acquired in February–April 2010/11), secondary images (Google Earth images, Landsat, etc.),
ancillary maps (LRMP and topographical maps) and the FRA Nepal eld inventory data. The
imageries were processed for geometric and atmospheric correcons prior to forest cover
analysis and mapping.
Area by land cover classes—Forest, Other Wooded Land (OWL), and Other Land (OL)—was
esmated by using the forest cover maps. Also, the results on area by protecon category, area
by districts, and forest patches were esmated by using the forest cover maps.
Geometric Correcon
The RapidEye Level 1b imagery was ortho-reced by using Toun’s Model (Toun, 2004), with
ground control points and digital elevaon model. The ground control points were idened by
using road and river features from the Naonal Topographical Map Data. The digital elevaon
model was also generated from the Naonal Topographical Map Data by using contours and
spot levels. Independent check points were xed to assess the level of accuracy (Figure 5). The
planimetric accuracy of the ortho-reced images was 9.81 m (≈1.96 pixels RMSE) for the 1,355
ground-control points for 48 RapidEye scenes covering the enre country.
STATE OF NEPAL’S FORESTS 11
Figure 5: RapidEye image les and ground control points used for mapping
Atmospheric Correcon
Atmospheric correcon was made to minimise the eects of atmospheric haze and terrain
shadows by using topographical normalisaon and Bidireconal Reectance Distribuon
Funcon (BRDF) correcon of the ATCOR3 model dened by Richter (1998) and given in Equaon
1. Atmospheric correcon was made for cloud and haze removal for the imageries covering Terai
region. For the imageries covering Churia, Middle Mountains, High Mountains and High Himal,
atmospheric correcon was made for haze and cloud removal, and BRDF correcon was made
to remove terrain shadow.
Equaon 1: Bidireconal reectance distribuon funcon (BRDF)
G = (cos βi / cos βT)1/2
where,
G = BRDF factor
βi = incidence angle
βT = threshold angle
3.1.1 Land Cover Mapping
Land cover was mapped by adopng a hybrid approach using automated image classicaon
system and supported by extensive visual interpretaon (GOFC-GOLD, 2013). Images were
classied by applying segmentaon and automated object-based image analysis method
(Baatz and Schape, 2000) using eCognion soware (Version 8). Four spectral properes
were considered: (i) mean pixel values of green, red, red-edge and near-infrared bands; (ii) a
derived Normalised Dierence Vegetaon Index (NDVI); (iii) principal components; and (iv) the
homogeneity texture of the near-infrared band. Randomly sampled reference training sets from
the Phase 1 plots were used to classify land as Forest, OWL and Other Land (non-forest) along
with addional eld observaon data for OWL and shrub classicaon. Forest, OWL and Other
Land areas were classied by dening a ‘containment membership funcon’ for threshold values
for all four properes. In order to improve classicaon accuracy, on-screen post-classicaon
visual interpretaon was carried out on the classied Forest, OWL (including shrub) and Other
Land by using high-resoluon images in Google Earth. In addion, eld vericaon surveys were
STATE OF NEPAL’S FORESTS
12
undertaken throughout Terai, Churia and Middle Mountains regions, in order to delineate OWL
(including shrub) as well as to recfy errors in forest cover classicaon.
3.1.2 Forest Fragmentaon Mapping
Fragmentaon of forest patches and the sizes of those patches were analysed and mapped over
the classied forest cover for each physiographic region. Spaally conguous forest patches that
fullled the criteria for forest were categorised based on their sizes, which ranged from less than
2 ha to greater than 50,000 ha. The frequency of occurrence and total area covered in each size
category were analysed to assess the distribuon and area of forest fragments. The results of the
assessment of forest patches are presented in the reports for physiographic regions.
3.1.3 Forest Type Mapping
An approach based on machine learning and classicaon was developed for naonal level wall-
to-wall forest type classicaon and mapping. The approach used Classicaon and Regression
Tree (CART) with threefold cross-validaon algorithm. In the CART process, Landsat 8 (acquired
during October/November 2013) imagery variables (6 MSS bands, 8-Grey Level Co-occurrence
Matrix) along with DEM parameters (elevaon range, slope, aspect) were used as predictor
variables. The machine learning CART process was trained by using FRA eld inventoried forest
type data from the PSPs (n = 907) selected randomly (80% intensity with forest types as strata)
within individual Landsat 8 scene coverage area. The CART process uses binary regression
algorithm to classify each image segment into designated forest types. The classied forest type
was cross-validated by using the remaining 20% PSP forest type plots (n = 597).
3.1.4 Accuracy Assessments of Mapping
Accuracy assessment for land/forest cover mapping at each physiographic region was done by
comparing randomly sampled cover classes on the maps with independent ground truth data (n
= 1,894) of which 1,522 were inventory plots (PSPs) and 372 purposively sampled observaon
plots for OWL (including shrubs). Addional purposively selected observaon plots were used to
supplement the limited number of OWL plots in the inventory.
For wall-to-wall forest type classicaon and mapping, cross validaon was done by using the
randomly selected inventory plots (n = 597). Error matrices were analysed to assess overall
accuracy and kappa stascs were used to test the reliability and standard errors.
3.2 Forest Resource Inventory
3.2.1 Sampling Design
A two-phased straed systemac cluster sampling design was adopted. The ve physiographic
regions dened by the Department of Survey—High Himal, High Mountains, Middle Mountains,
Churia and Terai—were used as strata. A hybrid approach was adopted in the forest inventory
through interpretaon of satellite images at the rst phase and measurement of forest
characteriscs in the eld at the second phase (Figure 6). Detailed methodology is presented in
the respecve reports for the physiographic regions.
STATE OF NEPAL’S FORESTS 13
Figure 6: Layout of sample plot /cluster
A two-phased sampling method was used in order to concentrate the eld measurements on
forested clusters and to avoid walking long distances to reach a cluster without forest plots.
Whilst a wide variety of biophysical forest parameters were assessed; the 10% accuracy at 95%
condence limit was set for stem volume.
First Phase Sampling
The rst phase of sampling was undertaken as a desk study using high-resoluon satellite imagery
(RapidEye) and Google Earth (along with topographic maps) with a grid of 4 km x 4 km laid over
it. The evenly distributed rst phase sampling clusters were posioned at the grid nodes and the
plots were examined closely on the satellite image, topographic map sheets and digital elevaon
model. Reachability (dened as altude <4,000 m and slope <100%) and accessibility were then
assessed for each of these rst phase sample plots.
The rst of the six sample plots in each cluster was situated at a grid node and the two other
plots were each mapped an addional 150 m northward of that plot. A parallel set of three plots
was situated 300 m east of the rst three (Figure 6).
Clusters were numbered by columns from west to east and by rows from south to north across
the country. Altogether, 9,230 clusters were idened on the imagery. Within each cluster, plots
were numbered from south to north, assigning plot numbers 1, 2 and 3 to the west and 4, 5, and
6 to the east. In some cases, where plots crossed internaonal borders, fewer than six plots were
idened. In this way, 55,358 viable plots inside Nepal were idened by column, row and plot
number (Table 4).
High Himal
STATE OF NEPAL’S FORESTS
14
Table 4: Distribuon of the rst phase sample plots by physiographic region
Physiographic region 1st phase plots
Terai 7,533
Churia 7,132
Middle Mountains 16,139
High Mountains 11,307
High Himal 13,247
Total 55,358
Each plot was classied according to FAO Land Use Classes and reachability through visual
interpretaon of Google Earth imagery. The nine land use classes of FAO were:
i. Forest
ii. Other wooded area
iii. Agricultural area with tree cover
iv. Agricultural area without tree cover
v. Built-up area with tree cover
vi. Built-up area without tree cover
vii. Roads
viii. Other area
ix. Water
The land use classes (iii) to (ix) were categorised as Other Land in this assessment.
Second Phase Sampling
The second phase sample was a sub-sample of the rst phase sample. Clusters selected for the
second phase were measured in the eld. A total of 450 clusters (1,553 plots) in Forest were
measured. Altogether, 2,544 sample plots including 1,553 plots in Forest and 105 plots in OWL
were permanently established and assessed whereas 886 plots on Other Land were measured
(Table 5 and Figure 7). Details of second phase sampling for each physiographic region can be
found in the respecve physiographic region reports.
Table 5: Distribuon of permanent sample plots and clusters
Physiographic region Permanent sample plots No of forest
clusters
Forest OWL OL
Terai 175 5 160 56
Churia 477 11 219 109
Middle Mountains 433 63 377 146
High Mountains 421 21 115 139
High Himal 47 5 15
Total 1,553 105 886 450
STATE OF NEPAL’S FORESTS 15
Figure 7: Distribuon of sample plots
3.3 Sample Plot Design
Each sample plot consisted of four concentric circular sample plots (CCSP) of dierent radii, four
vegetaon sub-plots, four shrubs and seedlings sub-plots, and four soil pits. The plot design for
tree measurement is given in Table 6 and Figure 8.
Table 6: Size and area of CCSP of dierent radii with DBH limits
S.N. Plot radius (m) DBH limit (cm) Area (m2)
1 20 >30.0 1,256.63
2 15 20.0–29.9 706.86
38 10.0–19.9 201.06
4 4 5.0–9.9 50.27
Other sub-plots were established to assess the status of seedlings, saplings, shrubs and herbs.
Seedlings, saplings and shrubs were measured in four circular sub-plots, each with a radius of 2
m, located 10 m away from the centre of the plot in each of the four cardinal direcons (North,
East, South and West). Species-wise stem counng and mean height esmaons were carried
out for tree and shrub species having DBH less than 5 cm. Informaon on non-woody vascular
plants was collected from four 1 m2 plots, each located 5 m away from the centre in the four
cardinal direcons. Dead wood was assessed in a circular plot with a radius of 10 m from the
plot centre.
High Himal
Churia
STATE OF NEPAL’S FORESTS
16
Figure 8: Layout of concentric circular permanent sample plots and sub-plots
Fourteen categories of natural and anthropogenic forest disturbances were assessed through
eld observaons of both their occurrence and intensity (severe, moderate, minor) in the 20
m radius plot. Four soil pits per forest stand were prepared in order to identy soil texture
and to determine soil stoniness. Soil, lier and debris were collected as composite samples by
combining the materials collected at all soil pits.
The sample plots were navigated in the eld by using hand-held GPS and located with a dierenal
GPS (DGPS). Height of the sample tree was measured by using Vertex IV and Transponder T3.
Crown cover was esmated by using spherical densiometer. Calipers and D-tapes were used to
measure diameter.
3.3.1 Volume and Biomass Esmaon
Stem volume was esmated by using DBH and total height of the tree. Height models were
prepared for tree species and species groups by using the data collected from sample trees. A
non-linear mixed-model approach was used to establish the relaonships between the DBHs
and total heights of trees using the ‘Lmforpackage in R Soware (Mehtatalo, 2012). A model for
predicng tree DBH from stump diameter was also developed so that the volume and biomass of
trees that had been felled could be esmated. Details on tree-height model of dierent species
and their accuracy are given in individual reports for physiographic regions.
The volume equaons developed by Sharma and Pukkala (1990) and the biomass models
prescribed by the MPFS (1989) were used to esmate the volume and biomass of standing trees.
The air-dried biomass values obtained using these equaons were then converted into oven-
dried biomass values using a conversion factor of 0.91 (Chaturvedi, 1982; Kharal and Fujiwara,
2012) and a carbon-rao factor of 0.47 (IPCC, 2006a, b). The volume and biomass of seedling and
sapling having DBH less than 10 cm were not included.
STATE OF NEPAL’S FORESTS 17
Stem volume esmaon
The following allometric equaon (Equaon 2) developed by Sharma and Pukkala (1990) was
used to esmate stem volume over bark:
Equaon 2: Stem volume
ln(v) = a + b ln(d) + c ln(h)
where,
ln = Natural logarithm to the base 2.71828.
V = Volume (dm3) = exp [a + b×ln(DBH) + c×ln(h)]
d = DBH in cm
h = Total tree height in m
a, b and c are coecients depending on species
Note: Values were divided by 1,000 to convert them to m3
The volumes of individual broken trees were esmated by using a taper curve equaon developed
by Heinonen et al. (1996).
Tree-stem biomass esmaon
Tree-stem biomass was calculated by using Equaon 3 and species-specic wood-density values
(Sharma and Pukkala, 1990; MPFS, 1989).
Equaon 3: Tree stems biomass
Stem biomass = Stem vol. × Density
where,
Stem vol. = Stem volume in m3
Density = Air-dried wood density in kg/m3
Tree-branch and foliage biomass esmaon
The separate branch-to-stem and foliage-to-stem biomass raos prescribed by MPFS (1989)
were used to esmate branch and foliage biomass from stem biomass. Dead trees were not
taken into account for the esmaon of branch and foliage biomass.
The total biomass of individual trees was esmated by using Equaon 4.
Equaon 4: Total biomass of each individual tree
Total biomass = Stem biomass + Branch biomass + Foliage biomass
Below-ground biomass esmaon
This esmaon was calculated by using default value as recommended by IPCC (2006). The
rao 0.25 was used by taking an average of the ve dierent forest types (primary tropical/sub-
tropical moist forest = 0.24, primary tropical/sub-tropical dry forest = 0.27, conifer forest having
more than 150 t/ha above-ground biomass = 0.23, other broadleaved forest having 75 t/ha to
150 t/ha above-ground biomass = 0.26, and other broadleaved forest having more than 150 t/
ha above-ground biomass = 0.24). The biomass of seedlings and saplings having DBH less than
10 cm was not incorporated.
STATE OF NEPAL’S FORESTS
18
3.3.2 Reliability of Results
The mean value at naonal level was esmated by using weighted method considering area
and mean value of the physiographic regions. Stem volume per hectare was considered as the
main variable while assessing the reliability of the results. Reliability was esmated in terms of
standard error of the mean stem volume. The desired accuracy was 10% at 95% condence level.
The variance of mean volume esmate in forest was esmated by using the variance esmator
of a rao esmator:
Equaon 5: Variance of mean volume esmate (for individual physiographic region)
where,
np=number of clusters with at least one forest plot
mp,i=number of forest plots in cluster i
xi=sum of plot level volumes in cluster i, m3/ha
=mean volume in forest
p refers to physiographic region.
The variance of mean volume esmate in forest at naonal level was calculated with an esmator
of straed sampling (Cochran, 1977):
Equaon 6: Variance of mean volume esmate (for naonal level)
where,
proporon of physiographic region-wise forest area with respect to total
forest area of Nepal.
3.4 Forest Soils
3.4.1 Sampling of Soil
Soil samples were collected from four soil pits dug at each cardinal direcon, 21 m away from the
CCSP-centre, soil pits were dug within 2 m x 2 m area so as to collect undisturbed soil samples.
The samples were collected by using a 100 mm long, slightly conical cylinder corer with a lower
diameter of 37 mm (at its cung edge) and an upper diameter of 40 mm; the volume of each
soil sub-sample collected was 107.5 cm3.
Composite soil samples from three layers i.e., 0–10 cm, 10–20 cm and 20–30 cm depth were
collected in separate plasc bags for each layer (Figure 7). The fresh mass of composite sample
was determined with the accuracy of 1 g. The samples were brought to DFRS Soil Laboratory
and kept separately in order to facilitate assessment of the within-site variability of soil organic
carbon (SOC). The relave volume occupied by stones in the soil was esmated occularly by
observing the soil pit-walls by using the FAO Guidelines (FAO, 2006).
STATE OF NEPAL’S FORESTS 19
3.4.2 Sampling of Lier and Woody Debris
Lier and debris fracons were separately collected from the 1 m2 circular plots at the locaon
of each soil pit before it was dug to make their composite samples (Figure 9). A value of zero was
recorded for pits without any lier or woody debris on the surface to ensure that the esmate of
average lier and debris mass per unit area would be accurate. The total composite fresh mass
of both lier and debris was weighed in the eld to an accuracy of 1 g. As the total volume of all
4 m2 areas (the total of the four 1 m2 plots) was relavely large, small representave sub-samples
were set aside so that their dry mass could be determined in the laboratory.
Figure 9: Collecon of composite samples of lier, debris and soil from a plot
3.4.3 Analyses in the Laboratory
Analysis of soil physical parameters and proporon of Organic Carbon (OC)
For calculaon of soil OC stock per volume and area, dry mass of undisturbed soil with known
volume is needed. For that purpose, the composite soil samples were rst air-dried to stabilise
decomposion of organic maer, and later oven-dried to constant weight. The oven-dried
sample was immediately weighed for total bulk density and then sieved through a 2 mm sieve,
thus the soil ne fracon (FF) was obtained. The volume of coarse fracon (not passing the sieve)
was determined from water replacement method. The bulk density of the soil ne fracon was
then calculated by eliminang the volume of the coarse fracon, because the stone parcles are
void of OC. The bulk-density of the ne soil fracon for each soil layer was used to calculate the
organic carbon stock in each of the 10 cm soil layers.
Soil organic carbon content was analysed by using the paral wet combuson method (Walkley
and Black, 1934), with a correcon factor of 1.33 to adjust for the total OC. Prior to analysis, the
soil was passed through a 0.5 mm sieve for beer homogenisaon.
Esmaon of soil organic carbon stock
The SOCFF stock was calculated by mulplying the dry soil bulk density (g/cm3) by the proporon
of OC as analysed in the ne fracon (FF) of soil. The nal SOCFF, adj value was obtained aer
adjusng the laboratory results with a consideraon of the proporon (Stone%) of stoniness
STATE OF NEPAL’S FORESTS
20
determined in the eld:
SOCFF, adj = (100-Stone%/100) * SOCFF.
This adjustment was needed because no organic carbon is found in stones and because laboratory
analyses give the organic carbon content only for the ne soil fracon (SOCFF). Aer adjustment,
the SOC stock results were extrapolated per hectare.
Analysis of organic carbon in lier and woody debris
Organic carbon stock in lier and woody debris fracons was obtained on the basis of the total
fresh mass collected from a known area as measured in the eld. First, the dry mass of lier and
woody debris sub-sample was obtained by oven-drying it to constant weight. Second, the total
oven-dried weight of the lier and debris was esmated by mulplying the rao of oven-dried to
fresh weight of the lier and debris sub-samples. The total OC content of lier and woody debris
fracons was then obtained by summing the respecve dry mass esmates per m2, mulplied by
0.50, a carbon content constant suggested by Pribyl (2010).
3.5 Forest Biodiversity
The lists of ora species obtained from the eld sample plots were veried by using various
sources (Edwards, 1996; DPR, 2007; Press et al., 2000 and Bhuju et al., 2007). Frequency of tree
species (the proporon of sampling units containing a given tree species) was calculated by using
Equaon 7.
Equaon 7: Tree species frequency
where,
fi = Frequency of species i
ni = Number of plots on which species i occurred
N = Total number of plots studied
3.6 Forest Disturbance
A disturbance is dened as a temporary change in average environmental condions that cause
a pronounced change in an ecosystem. Intensity of each disturbance aecng the growth of
vegetaon in each sample plot were recorded and analysed at the naonal level. The types of
disturbances were recorded by using the following categories:
No disturbance: No signs of signicant disturbance observed
Landslide: Signs of landslide and/or ooding observed
Grazing: Presence of the hoofmarks and dung of animals, broken tops of seedlings
and saplings, signs of trampling, disturbed forest lier
Lopping: Cung of the side branches of trees for fodder
Leaf lier collecon: Collecon of dead leaves on the forest oor
Bush cung: Sign of cung of shrubs, bushes and seedlings
Forest re: Sign of forest re observed caused by natural and human acvies
Encroachment: Encroachment in forest for culvaon and plantaon
Resin tapping: Tapped trees, ordinarily pines, were idened by cuts made in the boles
of trees to enable resin to ooze out
Lathra cung: Cung of saplings and poles up to 30 cm DBH
STATE OF NEPAL’S FORESTS 21
Tree cung: Cung of trees ≥30 cm DBH
Insect aack: Plant leaves with signs of insect aacks (e.g. holes, nests, etc.)
Plant parasites: Presence of parasic plants in trees
Plant disease: Disease caused mainly by fungi (e.g. black rot) or bacteria (e.g. rong).
If a tree was rong due to resin-tapping, the disturbance was recorded
as resign-tapping, not as plant disease
Wind, storm, hail: Sign of trees broken and erosion on forest oor caused by wind, storm,
hail
Other human-induced disturbances: Disturbances by humans other than those described
above (e.g. removing the bark from the base of a tree, snaring, foot
trails, forest roads, etc.)
The intensity levels of the above-menoned disturbances were classied as below:
Intensity level 0: No signicant disturbance
Intensity level 1: Minor disturbance (lile or no eect on trees and regeneraon, less
than 10% of trees and seedlings aected)
Intensity level 2: Moderate disturbance (tangible eect on trees and regeneraon, 10–
25% of trees and seedlings aected)
Intensity level 3: Severe disturbance (signicant eect on trees and regeneraon, more
than 25% of trees and seedlings aected)
STATE OF NEPAL’S FORESTS
22
4.1 Land Cover Mapping
4.1.1 Visual Interpretaon in the First Phase Sampling
On-screen visual interpretaon as a pre-processing step makes it possible for an interpreter to
easily integrate the dierent characteriscs of objects (e.g. surface texture) visible in an image
and benet from direct knowledge of the context. Unlike digital classicaon methods, such
interpretaon does not require specialised soware. Some of the images interpreted in 2010
were partly from 2003–2005, and land cover changes in the intervening years could have caused
some discrepancies. Google Earth images might have some local geometrical distorons which
can lead to misinterpretaon of the boundaries between two land cover types, and visual
interpretaon may be distorted by human error in classifying land cover.
4.1.2 Forest Cover Classicaon and Analysis
Remote sensing-based mapping of vegetaon and its types is a challenging task to begin with
and these challenges are exacerbated by the dicult and varied terrain and climate of Nepal.
With a scienc and technically sound approach, appropriate remote sensing materials and the
support of reliable and extensive ground samples, mul-source mapping of vegetaon/forest
can be achieved with a good degree of accuracy and reliability. Several technical challenges were
faced while mapping forest and non-forest areas in all physiographic regions. These are given
below:
 The classicaon and analysis of forest cover was complicated by the fact that some
deciduous trees, e.g., Shorea robusta, Acacia catechu and Anogeissus lafolia were defoliated
during the period of image acquision. Classicaon and analysis of such forest cover was
challenging (Figure 10).
4
LIMITATIONS
STATE OF NEPAL’S FORESTS 23
Figure 10: Defoliated Acacia catechu forest in Mid-Western Nepal
 Spaal heterogeneity of forest stands and fuzziness of their boundaries might have
introduced errors into their classicaon and delineaon (Figure 11).
Figure 11: Undened (fuzzy) boundaries of forest areas
 Mapping and delineaon of shrub areas was challenging due to the limitaons of the images
used and reectance characteriscs of dierent species. Mapping of shrubs had to rely on
visual interpretaon method with the aid of very high-resoluon Google Earth imageries as
ancillary data to supplement automac classicaon using CART machine learning method
adopted in the rst phase plots in the High Mountains and High Himal regions.
 Due to the dicult terrain and inaccessibility, independent ground vericaon for mapping
could not be conducted in High Mountains, High Himal and the central part of Middle
Mountains. Instead, the validaon work had to rely heavily on visual interpretaon of Google
Earth images and independent assessments using forest inventory plots.
 Young regeneraon and recent plantaon might have been classied as Other Land because
they are not spectrally dierent from the surrounding land cover.
STATE OF NEPAL’S FORESTS
24
4.2 Forest Resource Inventory
The methodology was designed to collect naonal-level data on per hectare stem volume or
biomass of forests with 10% accuracy at 95% condence limit. This is the reason why reliability
of other ndings (number of stems and volume by species, forest type, quality class; number
of seedlings and saplings, biodiversity; soil carbon, etc.) may not be within the target accuracy
level, and they are indicave values.
The High Mountains and the High Himal data were combined for analysis and reporng due
to insucient number of sample plots measured in High Himal region because of dicult
terrain and weather condion. Moreover, the forest types and species composion of both the
regions are similar. The number of measured sample plots in both the physiographic regions is in
proporon to their respecve total forest area.
The tree aribute gures for physiographic regions were calculated for all stems >5 cm DBH while
naonal-level calculaon considered stems >10 cm DBH to make the naonal results comparable
with previous assessments and for internaonal reporng. This results in dierences between
the values calculated for naonal reporng and for physiographic region-wise reporng.
4.3 Assessment of Forest Soils
4.3.1 Soil Organic Carbon
Soil sampling was done only in the sample plots that were designed for naonal-level forest
inventory. Therefore, it might not have represented all the micro-site variability for all
physiographic regions. Seasonal variability in eld work and soil sampling might also have
aected soil organic carbon esmaon. Some level of bias may have occurred when calculang
SOC from few accessible plots especially in High Mountains and High Himal and averaging the
value for the whole region.
4.3.2 Lier and Woody Debris
Esmaon of the lier and debris stock may have been impacted by the me of assessment. The
inventory was mostly conducted over the long dry period outside of the monsoon, and there
could be seasonal dierences in lier-fall between visits to dierent areas and elevaons.
STATE OF NEPAL’S FORESTS 25
5.1 Land Cover
5.1.1 Land Cover by Physiographic Region
According to land cover mapping, Forest covers 5.96 million ha, i.e., 40.36% of total area of
Nepal. Similarly, Other Wooded Land (OWL) covers 0.65 million ha (4.38%) and Other Land
covers 8.16 million ha (55.26%). Within OWL, shrub covers 0.12 million ha (0.79%) and areas
with tree crown cover 5–10% covers 0.53 million ha (3.59% of the total area). Both Forest and
OWL together cover 6.61 million ha, 44.74% of total area of the country (Table 7 and Figure 12).
Table 7: Land cover area by physiographic region (ha)
Physiographic
region
Forest Other Wooded Land (OWL)
Other Land Total1
Tree crown
cover 5–10% Shrub Total OWL
Terai 411,580 5,573 3,930 9,502 1,595,916 2,016,998
Churia 1,373,743 22,336 336 22,672 501,848 1,898,263
Middle
Mountains 2,253,807 29,308 32,979 62,287 1,993,302 4,309,396
High Mountains
and High Himal 1,922,909 473,850 79,581 553,431 4,072,426 6,548,766
Naonal total 5,962,038 531,066 116,826 647,892 8,163,492 14,773,423
Note: For inventory calculaon, the area of OWL and OL was considered as the area below 4,000 m altude
in High Mountains and High Himal i.e. 484,357 ha and 1,197,005 ha, respecvely. In total, OWL and OL
were calculated as 578,818 ha and 5,288,071 ha, respecvely.
Due to rounding-o of area gures, there are slight dierences in their total.
5
RESULTS
1 This area indicates the total mapped area based on the generalised internaonal boundary data from the Department
of Survey. The ocial area of Nepal is 147,181 km2.
Figure 12: Land cover map of Nepal
STATE OF NEPAL’S FORESTS 27
Out of the total Forest, 37.80% lies in Middle Mountains physiographic region, 32.25% in High
Mountains and High Himal, 23.04% in Churia and 6.90% in the Terai. Out of the total OWL, Terai,
Churia, Middle Mountains, and High Mountains and High Himal physiographic regions share
1.47%, 3.50%, 9.61% and 85.42%, respecvely (Figure 13).
Figure 13: Proporon of land cover in each physiographic region
5.1.2 Land Cover by Development Region and District
Mid-Western Development Region (MWDR) contains 26.68% of the total Forest of Nepal. Far-
Western Development Region (FWDR) has the lowest proporon of Forest i.e., 16.94% of the
total Forest of Nepal. Other Wooded Land (OWL) ranges from 7.17% in Central Development
Region (CDR) to 39.46% in MWRD (Table 8). District-wise land cover area is given in Annex 1.
Table 8: Land cover by Development Region (ha)
Development
Region Forest
Other Wooded Land (OWL)
Other
Land Total
Tree crown
Cover 5–10% Shrubs Total
OWL
EDR 1,072,379 82,807 18,392 101,198 1,679,374 2,852,952
CDR 1,268,667 40,264 6,248 46,512 1,432,112 2,747,292
WDR 1,020,065 87,117 21,522 108,639 1,816,150 2,944,852
MWDR 1,590,722 194,439 61,204 255,643 2,404,594 4,250,959
FWDR 1,010,207 126,439 9,461 135,900 831,261 1,977,367
Naonal total 5,962,038 531,066 116,826 647,892 8,163,492 14,773,423
5.1.3 Forest Cover by Physiographic and Development Region
Out of the total Forest in Terai physiographic region, FWDR has the highest proporon (30.93%)
whereas Western Development Region (WDR) has the lowest (11.47%). Similarly, out of the total
Forest in Churia, CDR has the highest proporon (31.30%), whereas Eastern Development Region
(EDR) has the lowest (12.62%). Forest in Middle Mountains physiographic region is more or less
evenly distributed in all the Development Regions. Out of the total Forest in High Mountains and
High Himal physiographic region, MWDR has the highest proporon (34.43%) of Forest whereas
CDR has the lowest (13.74%) (Table 9 and Figure 14).
Churia
STATE OF NEPAL’S FORESTS
28
Table 9: Forest cover by physiographic and Development Region (ha)
Development
Region Terai Churia Middle
Mountains
High Mountains
and High Himal Total
EDR 56,220 173,298 481,314 361,547 1,072,379
CDR 95,219 430,029 479,295 264,124 1,268,667
WDR 47,209 175,133 440,204 357,519 1,020,065
MWDR 85,618 414,795 428,187 662,122 1,590,722
FWDR 127,314 180,489 424,807 277,597 1,010,207
Naonal total 411,580 1,373,743 2,253,807 1,922,909 5,962,038
Figure 14: Proporon of forest cover by development and physiographic region
5.1.4 Forest Cover Inside and Outside Protected Area by Physiographic Region
Out of the total Forest of the country, 4.93 million ha (82.68%) lies outside Protected Areas and
1.03 million ha (17.32%) inside Protected Areas. Within the Protected Areas, Core Areas have
0.79 million ha Forest and Buer Zones have 0.24 million ha. Of the total Forest inside the Core
Areas, High Mountains and High Himal regions together have the highest share (57.95%) and
Middle Mountains region has the lowest (2.10%) (Table 10).
Table 10: Forest cover inside and outside Protected Areas by physiographic region
Physiographic region Outside PAs Protected Area (ha) Total
Core Area Buer Zone
Terai 314,660 69,847 27,074 411,580
Churia 1,043,194 246,750 83,799 1,373,743
Middle Mountains 2,226,273 16,669 10,865 2,253,807
High Mountains and High Himal 1,345,309 459,240 118,360 1,922,909
Naonal total 4,929,436 792,506 240,098 5,962,038
STATE OF NEPAL’S FORESTS 29
5.1.5 Forest Cover by Forest Types
According to forest cover mapping, the Terai Mixed Hardwood (TMH) forest type has the highest
coverage (24.61%) followed by the Upper Mixed Hardwood (UMH) (18.23%). Similarly, the share
of Shorea robusta and Pinus roxburghii forest types are 15.27% and 8.45%, respecvely. Nearly
60% of the total forest area is composed of mixed types (Figure 15 and 16).
Figure 15: Proporon of forest types at naonal level
Figure 16: Forest type map of Nepal
STATE OF NEPAL’S FORESTS 31
5.1.6 Accuracy Assessments
Land/Forest Cover Mapping
The land cover classes (Forest, OWL and OL) observed in the eld were compared with the
classied land cover classes. The comparison revealed an overall accuracy of 85.16%, a Cohen’s
Kappa (κ) of 0.72, and a Kappa standard error of 0.02 for the naonal level wall-to-wall land
cover map (Table 11).
Table 11: Error matrix of land cover map using independent ground vericaon samples
Classied class Land cover class (ground truth) Users’
accuracy (%)
Commission
error (%)
Forest OWL OL Total
Forest 1,096 53 16 1,165 94.08% 5.92%
OWL 56 362 34 452 80.09% 19.91%
Other Land 49 73 155 277 55.96% 44.04%
Total 1,201 488 205 1,894
Producer’s accuracy (%) 91.26% 74.18% 75.61%
Omission error (%) 8.74% 25.82% 24.39%
Overall accuracy 85.16%
Forest Type Mapping
The accuracy assessment yielded an overall accuracy of 69.85% (95% condence limit, range
66.08%–73.61%; Standard Deviaon (SD) = 0.02; Coecient of Variance (CV) = 2.7%). The Kappa
stascs (k) was obtained as 0.63 (95% condence limit, range = 0.58–0.67; SD = 0.02 and CV =
3.6%). These gures indicate good overall accuracy.
5.1.7 Comparison with Previous Assessments
The FRA (2010–2014) results cannot be stascally compared with the previous surveys due to
methodological dierences. However, among the six naonal-level assessments in the last four
decades, FRA (2010–2014) found the largest coverage of forest and/or shrub in Nepal (Table 12).
Table 12: Forest cover found by dierent assessments (%)
Land cover
LRMP
1978/79
NRSC
1984
Master Plan
1985–1986
NFI
1994
DoS 1995 FRA
2010–2014
Forest 38.0 35.9*37.4 29.0 38.3 40.36
Shrub 4.7 - 4.8 10.6 - 4.38**
Total 42.7 35.9 42.2 39.6 38.3 44.74
* Includes some shrub area; **OWL
Although esmaon of these six assessments does not show a clear trend of change in forest
cover, the gure of forest area esmated by FRA (2010–2014) is more than that of NFI (1994).
This may be aributed to the following three factors:
First, in NFI (1994), the area esmates were compiled from dierent assessments with a larger
minimum mapping unit (MMU) than that of FRA (2010–2014), which applied uniform method
based on high-resoluon image classicaon with MMU of 0.5 ha. Therefore, it is likely that the
smaller forest patches which were excluded in the previous inventory could have been included in
the recent one. Second, as observed by various studies at local level (Niraula et al. 2013; Gautam
et al. 2002; Poudel et al. 2015), forest area in the country, parcularly in the mountains, may
have increased due to community forestry intervenon. Third, forest area may have increased
STATE OF NEPAL’S FORESTS
32
due to abandonment of agricultural land, parcularly in the mountainous region. Studies show
that increasing migraon in the recent years has resulted in ‘unprecedented’ land abandonment
in the mountains (Poudel et al. 2014); it has contributed to increased forest and/or shrub cover
(Jaquet et al. 2015; Sharma et al. 2014). However, further studies are suggested to determine
the extent of contribuon of these factors to forest/shrub cover change at the naonal level.
Table 13 presents areas of forest by Development Region esmated by the three assessments.
Table 13: Forest cover by Development Region (in '000 ha)
Development Region LRMP
1978/79
NFI 1994 FRA
2010–2014
Eastern 948.7 736.1 1,072.4
Central 1,104.9 918.6 1,268.7
Western 924.0 734.3 1,020.1
Mid-Western 1,649.7 1,192.4 1,590.7
Far-Western 989.5 687.4 1,010.2
Naonal total 5,616.8 4,268.8 5,962.1
Note: NFI 1994 used 147,181 km2 as the total area of the country for its calculaons while LRMP and FRA
used the mapped areas calculated in their assessments i.e. 147,484 km2and 147,734 km2, respecvely.
5.2 Forest Inventory
5.2.1 Number of Stems in Forest (DBH <10 cm)
At the naonal level, the number of stems less than 10 cm DBH was 11,566 per hectare.
The average number of seedlings (<1.3 m height) was 10,095. The corresponding gures for
saplings (≥1.3 m height and <5 cm DBH) and bigger saplings (5–10 cm DBH) were 1,045 and 426,
respecvely (Table 14).
Table 14: Number of seedlings and saplings per ha in Forest by physiographic region
Physiographic region Seedlings/ha
(<1.3 m height)
Saplings/ha (≥1.3
m height and <5
cm DBH)
Saplings/ha (5–10
cm DBH) Total/ha
Terai 29,649 1,662 309 31,620
Churia 19,805 958 389 21,152
Middle Mountains 7,171 1,167 442 8,780
High Mountains and High
Himal
2,399 831 459 3,689
Naonal weighted
average
10,095 1,045 426 11,565
5.2.2 Number of Stems in Forest (DBH ≥10 cm)
In Nepal, the total number of stems with diameter ≥10 cm was 3,112.28 million, of which
2,563.27 million stems were in Forest, 17.49 million in OWL and 531.52 million in Other Land.
The forest in High Mountains and High Himal regions had the highest number of stems (1,012.43
million). Terai forest had the lowest number of stems (112.85 million). Similar paern was found
in the case of OWL. However, in Middle Mountains the largest number of stems was found in
Other Land (Table 15).
STATE OF NEPAL’S FORESTS 33
Table 15: Number of stems (≥10 cm DBH) by land cover class
Physiographic region
Forest Other Wooded Land Other Land
Stems /
ha
Total stems
(million)
Stems /
ha
Total stems
(million)
Stems /
ha
Total stems
(million)
Terai 274.19 112.85 50.31 0.48 25.14 40.11
Churia 342.46 470.45 35.49 0.80 65.72 32.98
Middle Mountains 429.29 967.53 52.34 3.26 187.42 373.59
High Mountains and
High Himal
526.51 1,012.43 26.74 12.95 70.87 84.84
Naonal weighted
average/ Total
429.93 2,563.27 30.22 17.49 100.51 531.52
The stocking of all DBH classes was the highest in High Mountains and High Himal regions. The
total number of stems per hectare was the highest in High Mountains and High Himal, and the
lowest in Terai physiographic region (Table 16).
Table 16: Number of stems/ha in Forest by DBH class and physiographic region
Physiographic region Pole Small-saw mber Large-saw
mber Total
10–20 cm 20–30 cm 30–50 cm ≥50 cm
Terai 167.40 49.31 35.61 21.87 274.19
Churia 215.78 61.76 48.79 16.12 342.46
Middle Mountains 298.20 80.98 39.32 10.79 429.29
High Mountains and
High Himal
349.32 96.05 53.18 27.96 526.51
Naonal weighted
average
286.67 79.23 45.72 18.32 429.93
An analysis of DBH classes of stems for the country revealed that the proporon of small trees
was higher than that of large ones (Figure 17).
Figure 17: Number of stems by DBH class
STATE OF NEPAL’S FORESTS
34
Of the total species measured in the forests of Nepal, 14 species make up more than 1% of the
total trees. In terms of the number of stems (≥ 10 cm DBH) per hectare, Shorea robusta was the
most frequently occurring species (65.00 stem/ha or 15.1%), followed by Rhododendron spp.
(57.82 stems/ha or 13.5%) (Figure 18).
Figure 18: Number of stems per hectare in Forest by common species
Number of stems by quality class is shown in Figure 19. High-quality sound trees were most
frequently enumerated in Middle Mountains region (121.77 stems/ha) and least frequently
enumerated in High Mountains and High Himal (101.71 stems/ha). Similarly, cull trees were
most frequently enumerated in High Mountains and High Himal (289.40 stems/ha) and least
frequently enumerated in Terai region (87.01 stems/ha).
Figure 19: Number of stems per hectare in Forest by quality class and physiographic region
STATE OF NEPAL’S FORESTS 35
Of the total number of stems by quality classes in Nepal’s forest, the number of cull trees was
the highest (1,181.87 million). The number of stems of high-quality sound trees as well as sound
trees was the highest in Middle Mountains region. High Mountains and High Himal region contain
the highest number of stems of cull trees (Table 17).
Table 17: Stem distribuon by quality class in Forest (million)
Physiographic region High-quality sound tree Sound tree Cull tree Total
Terai 49.68 27.35 35.81 112.85
Churia 161.85 137.15 171.44 470.45
Middle Mountains 274.45 274.94 418.13 967.53
High Mountains and High
Himal
195.58 260.36 556.49 1,012.43
Naonal total 681.57 699.81 1,181.87 2,563.26
5.2.3 Basal Area (≥10 cm DBH)
Basal area of stems (≥10 cm DBH) was 20.57 m2/ha in Forest, 1.40 m2/ha in OWL, and 2.49 m2/ha
in Other Land. The basal area in Forest and OWL was found to be the highest in High Mountains
and High Himal (28.49 m2/ha) and Terai region (3.72 m2/ha), respecvely (Table 18).
Table 18: Basal area by land cover class (m2/ha)
Physiographic region Forest Other Wooded Land Other Land
Terai 17.08 3.72 0.91
Churia 17.17 1.79 1.55
Middle Mountains 16.53 1.99 4.12
High Mountains and High
Himal
28.48 1.26 2.26
Naonal weighted average 20.57 1.40 2.49
In the Forest, the highest basal area (28.48 m2/ha) was in High Mountains and High Himal region,
and the lowest (16.53 m2/ha) in Middle Mountains. Total basal area gures in Terai, Churia and
Middle Mountains regions were found to be more or less similar. The basal area of all DBH
classes was the highest in High Mountains and High Himal (Table 19).
Table 19: Basal area (m2/ha) by DBH class
Physiographic region Pole Small-saw
mber
Large-saw
mber Total
10–20 cm 20–30 cm 30–50 cm ≥50 cm
Terai 2.62 2.28 4.21 7.97 17.08
Churia 3.37 2.92 5.62 5.26 17.17
Middle Mountains 4.78 3.74 4.36 3.66 16.53
High Mountains and High Himal 5.61 4.46 6.05 12.38 28.50
Naonal weighted average 4.57 3.68 5.19 7.14 20.57
5.2.4 Stem Volume (≥10 cm DBH)
The total stem volume with DBH ≥10 cm was 1,063.56 million m3 out of which 982.33 million m3
(164.76 m3/ha) was in Forest, 4.58 million m3 (7.91 m3/ha) in OWL and 76.65 million m3 (14.49
m3/ha) in Other Land. The Forest of High Mountains and High Himal region had the highest stem
volume (225.24 m3/ha) and that of Middle Mountains had the lowest (124.26 m3/ha). Similarly,
STATE OF NEPAL’S FORESTS
36
OWL of Terai region had the highest stem volume (30.59 m3/ha) and that of High Mountains and
High Himal region had the lowest (6.73 m3/ha) (Table 20).
Table 20: Total stem volume per ha (≥10 cm DBH) by land cover class
Physiographic region
Forest Other Wooded Land Other Land
Stem
volume
(m3/ha)
Stem vol-
ume (million
m3)
Stem
volume
(m3/ha)
Stem volume
(million m3)
Stem
volume
(m3/ha)
Stem
volume
(million
m3)
Terai 161.66 66.54 30.59 0.29 5.41 8.63
Churia 147.49 202.61 15.05 0.34 9.32 4.68
Middle Mountains 124.26 280.06 11.04 0.69 23.72 47.29
High Mountains and
High Himal
225.24 433.12 6.73 3.26 13.41 16.05
Naonal weighted
average/Total
164.76 982.33 7.91 4.58 14.49 76.65
The stem volume per hectare of large trees (>50 cm DBH) in High Mountains and High Himal
physiographic region (124.94 m3/ha) was parcularly noteworthy. In contrast, the volume of large
sized trees (>50 cm DBH) was only 37.12 m3/ha in Middle Mountains (Table 21 and Figure 20).
Table 21: Stem volume (m3/ha) in Forest by DBH class
Physiographic region Pole Small-saw
mber
Large-saw
mber
Total
stem
volume
10–20 cm 20–30 cm 30–50 cm ≥50 cm
Terai 16.87 17.41 40.08 87.29 161.66
Churia 19.56 21.19 49.96 56.78 147.49
Middle Mountains 25.29 25.47 36.38 37.12 124.26
High Mountains and
High Himal
26.63 27.40 46.28 124.94 225.24
Naonal weighted
average 23.82 24.55 42.96 73.44 164.76
Figure 20: Proporonal volumes by DBH class and physiographic region
STATE OF NEPAL’S FORESTS 37
Of the total stem volume calculated in the Forest, 16 species have more than 1% of the total
stem volume. At the naonal level, Shorea robusta had the highest stem volume (31.76 m3/ha or
19.28%), followed by Quercus spp. with 24.39 m3/ha or 14.80% (Table 22).
Table 22: Stem volume in Forest by species
SN Species Stem volume (m3/ha) Stem volume (%)
1Shorea robusta 31.76 19.28
2Quercus spp. 24.39 14.80
3Pinus roxburghii 11.62 7.05
4Rhododendron spp. 8.68 5.27
5Terminalia alata 7.70 4.67
6Abies spp. 7.57 4.59
7Pinus wallichiana 6.18 3.75
8Alnus spp. 5.86 3.56
9Tsuga dumosa 5.73 3.48
10 Schima wallichii 4.38 2.66
11 Castanopsis spp. 2.85 1.73
12 Betula ulis 2.65 1.61
13 Picea smithiana 2.36 1.43
14 Lyonia ovalifolia 2.36 1.43
15 Lagerstroemia parviora 1.75 1.06
16 Acer spp. 1.65 1.00
Stem volume per hectare by quality class and physiographic region is presented in Table 23. Stem
volume per hectare of high-quality sound trees as well as sound trees was the highest in High
Mountains and High Himal and the lowest in Middle Mountains region. Interesngly, the highest
per hectare volume of cull tree quality class was also found in High Mountains and High Himal.
Table 23: Stem volume (m3/ha) in Forest by quality class and physiographic region
Physiographic region Quality Class Total
High-quality sound tree Sound tree Cull tree
Terai 121.54 22.24 17.88 161.66
Churia 100.79 28.87 17.83 147.49
Middle Mountains 69.75 27.77 26.74 124.26
High Mountains and
High Himal
136.28 42.52 46.44 225.24
Naonal weighted
average
101.94 32.40 30.43 164.76
Among the three tree quality classes, the high-quality sound trees constuted the highest stem
volume in all regions (Table 24).
STATE OF NEPAL’S FORESTS
38
Table 24: Stem volume in Forest by quality class and physiographic region (million m3)
Physiographic region
Quality Class (million m3)
Total
High-quality sound
tree Sound tree Cull tree
Terai 50.02 9.15 7.36 66.54
Churia 138.46 39.66 24.49 202.62
Middle Mountains 157.20 62.60 60.26 280.06
High Mountains and High Himal 262.05 81.77 89.30 433.12
Naonal total 607.74 193.18 181.41 982.33
5.2.5 Above-ground Air-dried Tree Biomass
The total air-dried biomass of trees with DBH ≥10 cm was 1,243.62 million tonnes of which
1,159.65 million tonnes (194.51 t/ha) was in Forest, 6.30 million tonnes (10.88 t/ha) in OWL and
77.67 million tonnes (14.69 t/ha) in Other Land (Table 25).
Table 25: Tree component-wise total biomass by land cover class
Land cover class Tree component Air-dried biomass
(≥10 cm DBH) (t/ha)
Air-dried biomass
(≥10 cm DBH) (million tonnes)
Forest
Stem
Branch
Foliage
118.14
62.95
13.42
704.35
375.31
79.99
Total 194.51 1,159.65
OWL
Stem
Branch
Foliage
5.53
4.59
0.76
3.20
2.66
0.44
Total 10.88 6.30
Other Land
Stem
Branch
Foliage
9.49
4.53
0.67
50.19
23.94
3.54
Total 14.69 77.67
The above-ground air-dried biomass in Forest was 194.51 tonnes per hectare. The forests of High
Mountains and High Himal contained the highest above-ground biomass per hectare (271.46
tonnes, air-dried), whilst Middle Mountains Forests had the lowest (143.26 tonnes, air-dried).
The average above-ground oven-dried biomass in Nepal’s forest was 176.82 t/ha (Table 26).
Table 26: Above-ground air- and oven-dried biomass of tree component (t/ha)
Physiographic region Stem
biomass
Branch
biomass
Foliage
biomass
Total above-
ground air-
dried biomass
Total above-
ground oven-
dried biomass
Terai 134.49 47.55 7.98 190.02 172.74
Churia 122.24 42.59 7.38 172.21 156.55
Middle Mountains 89.21 44.37 9.68 143.26 130.24
High Mountains and High
Himal
145.62 102.57 23.27 271.46 246.78
Naonal weighted average 118.14 62.95 13.42 194.51 176.82
STATE OF NEPAL’S FORESTS 39
Total above-ground air-dried biomass of trees per hectare increased with an increase in DBH
class (Table 27).
Table 27: Above-ground air-dried biomass (t/ha) of tree component by DBH class
DBH class Stem biomass Branch
biomass Foliage biomass Total above-ground
air-dried biomass
10–20 17.19 7.56 2.26 27.02
20–30 17.42 8.41 2.18 28.00
30–50 31.39 15.64 3.25 50.29
50 52.13 31.34 5.73 89.20
Of the total air-dried biomass calculated in Forest, 16 species contribute more than one percent.
At the naonal level, Quercus species had the highest total air-dried biomass (46.09 t/ha or
23.70%), followed by Shorea robusta with 37.83 t/ha or 19.45% (Table 28).
Table 28: Total above-ground air-dried biomass in Forest by species
SN Species Total air-dried
biomass (t/ha)
Total air-dried
biomass (%)
1Quercus spp. 46.09 23.70
2Shorea robusta 37.83 19.45
3Rhododendron spp. 11.22 5.77
4Terminalia alata 10.58 5.44
5Pinus roxburghii 9.90 5.09
6Abies spp. 5.45 2.80
7Alnus spp. 5.31 2.73
8Pinus wallichiana 4.22 2.17
9Betula ulis 4.11 2.11
10 Schima wallichii 4.07 2.09
11 Castanopsis spp. 3.88 2.00
12 Lyonia ovalifolia 3.84 1.97
13 Tsuga dumosa 3.79 1.95
14 Acer spp. 2.33 1.20
15 Picea smithiana 2.30 1.18
16 Lagerstroemia parviora 2.17 1.12
5.2.6 Reliability of Inventory Results
Each sample cluster in Forest was allocated systemacally in all physiographic regions or strata.
Reliability of the inventory results in terms of stem volume per hectare was rst determined for
each stratum on the basis of which reliability of results for naonal level was determined. While
designing this assessment, 95% condence limit was set for the inventory result with the range
of plus or minus 10% of the stem volume or biomass (FRA Nepal, 2010). The standard error for
Forest was found to be 6.17 and percentage of error of mean stem volume was 7.34% at naonal
level (Table 29). This is within the reliability limits set out in the project document.
STATE OF NEPAL’S FORESTS
40
Table 29: Standard errors and condence limits in Forest for physiographic region
Physiographic
region
No. of
cluster
No. of
plot
Mean
stem
volume
(m3/ha)
Standard
error of
mean
Percentage
of error of
mean at 95%
CL
95% Condence
limits of mean
Terai 56 175 161.66 10.08 12.22 141.90 181.42
Churia 109 477 147.49 6.27 8.33 135.21 159.77
Middle Mountains 146 433 124.26 8.12 12.82 108.34 140.18
High Mountains and
High Himal
139 468 225.24 15.84 13.78 194.20 256.29
Naonal weighted
average/Total
450 1,553 164.76 6.17 7.34 152.67 176.86
5.2.7 Changes in Growing Stock in Two Assessment Periods
The number of stems with DBH ≥10 cm was 430 per hectare. The corresponding gure of NFI
1987–1998 was 408/ha. The increase, however, was observed only in the smaller trees (DBH
class 10–20 cm) (Table 30).
Table 30: Number of stems per hectare by DBH class in two inventories
Inventory DBH class Total
10–20 cm 20–50 cm 50 cm
NFI 1987–1998 244 143 21 408
FRA 2010–2014 287 125 18 430
Mean stem volume per hectare was found to be less in FRA 2010–2014 (165 m3/ha) than in NFI
1987–1998 (178 m3/ha). The proporon of stem volume of Shorea robusta, Terminalia alata and
Abies spp. showed a decreasing trend, while an increasing trend was observed for Quercus spp.,
Rhododendron spp., Pinus wallichiana and Schima wallichii (Table 31).
Table 31: Proporon (%) of stem volume available in two inventories by common species
Species Stem volume (NFI 1987–1998) Stem volume (FRA 2010–2014)
Shorea robusta 28.2 19.3
Quercus spp. 7.6 14.8
Pinus roxburghii 6.3 7.1
Rhododendron spp.3.3 5.3
Terminalia alata 7.6 4.7
Abies spp. 4.9 4.6
Pinus wallichiana 1.1 3.8
Schima wallichii 2.0 2.7
5.3 Carbon Stock
In Nepal, the total carbon stock in Forest, OWL and Other Land (OL) was 1,157.37 million tonnes
out of which Forest, OWL and OL constuted 1,054.97 million tonnes (176.95 t/ha), 60.92 million
tonnes (105.24 t/ha) and 41.48 million tonnes (7.84 t/ha), respecvely. In the case of OL, the
carbon content of only the tree component was esmated. Out of the total forest carbon stock,
tree component (live, dead standing, dead wood and below-ground biomass), forest soils, and
lier and debris made up 61.53%, 37.80%, and 0.67%, respecvely (Table 32).
STATE OF NEPAL’S FORESTS 41
Table 32: Carbon stock (t/ha) in Nepal
Land cover class Tree component Lier and debris Soil Total
Forest 108.88 1.18 66.88 176.95
OWL 5.81 0.45 98.98 105.24
OL 7.84 - - 7.84
The carbon stocks in forests of dierent physiographic regions are summarised in Table 33. The
average organic carbon in soil, lier and debris, and tree component (≥10 cm DBH) are 66.88 t/ha,
1.18 t/ha and 108.88 t/ha, respecvely. The highest soil organic carbon stock (114.03 t/ha) was
esmated in High Mountains and High Himal regions. SOC was the lowest in Churia region with an
average of 31.44 t/ha. The results from Middle Mountains region showed an average SOC stock
of 54.33 t/ha. SOC stock in the forests of the Terai was found to be slightly higher than in Churia.
Table 33: Soil organic carbon, lier and debris and tree component carbon stock in Forest
by physiographic region (t/ha)
Physiographic region SOC Lier and debris Tree component (≥10 cm DBH)
Terai 33.66 0.28 104.47
Churia 31.44 0.32 97.69
Middle Mountains 54.33 1.65 79.42
High Mountains and High Himal 114.03 1.44 152.36
Naonal average 66.88 1.18 108.88
The SOC stock in forest was found to increase with increasing altude especially in Middle
Mountains region (Figure 21).
Figure 21: Variability of SOC with the elevaon in dierent physiographic regions
STATE OF NEPAL’S FORESTS
42
5.4 Biodiversity
5.4.1 Tree Species Diversity
Altogether 443 tree species belonging to 239 genera and 99 families were recorded in the
sample plots. The highest number of taxa was found in Middle Mountains region and the lowest
in Terai region (Figure 22). Fabaceae (19 genera and 37 species) was the largest family followed
by Lauraceae (9 genera and 29 species). Other large families were Rosaceae (7 genera and 23
species) and Moraceae (4 genera and 21 species). In terms of genera, Ficus was the largest genus
comprising of 15 species followed by Acer and Litsea each comprising of eight species.
Figure 22: Number of families, genera and species of tree by physiographic region
5.4.2 Tree Species Occurrence
Shorea robusta was the most frequently occurring tree species in the FRA sample plots. It was
found in the Terai as well as Middle Mountains and was measured in 42% of the total sample
plots. In order of descending presence, the other major tree species were Terminalia alata
(28%), Schima wallichii (19%), Lagerstroemia parviora (18%), Rhododendron arboreum (16%),
Syzygium cumini (14%), Anogeissus lafolia (13%), and Pinus roxburghii (12%). Twenty-one tree
species occurred in more than 5% of the measured sample plots (Figure 23).
443
STATE OF NEPAL’S FORESTS 43
Figure 23: Occurrence of common tree species in Forest sample plots
5.5 Forest Disturbances
Nearly two-thirds of the total forest area in the country was aected by grazing. Tree cung,
bush cung, lathra cung, lopping and forest re were also common. Other anthropogenic
disturbances, such as bark removal from the base of a tree, snaring, foot trails, forest roads, etc.
were observed in about one-quarter of the surveyed forest areas (Figure 24).
Figure 24: Occurrence of forest disturbances
STATE OF NEPAL’S FORESTS
44
Among all the physiographic regions, Churia was observed to have the highest occurrence of
forest disturbance, parcularly grazing, forest re, landslide and cung of vegetaon. Tree
cung and lopping were the highest in forests of Terai region. Lier collecon was higher in
forests of the Middle Mountains region (Figure 25).
Figure 25: Proporonal occurrence of forest disturbances by physiographic region
STATE OF NEPAL’S FORESTS 45
Forest Resource Assessment of 2010–2014 has generated comprehensive and up-to-date naonal
level forest informaon which is summarised in this report. This report provides informaon on
wall-to-wall mapping of land cover (Forest, Other Wooded Land and Other Land), growing stock
(number of stems, basal area, volume, and biomass), carbon stock (of tree component, forest
soil, lier and debris), occurrence of tree species and forest disturbance.
Informaon generated from the assessment will help the government and concerned stakeholders
in decision-making towards sustainable forest resource management. The results are equally
important for internaonal reporng by Nepal under various mullateral environmental
convenons and for Global Forest Resource Assessment (GFRA).
During the assessment, permanent sample plots were established in all physiographic regions,
and re-measurement of these plots will provide a basis for assessing temporal changes in forest
characteriscs. The results will also serve as a baseline for REDD+ measurement, reporng and
vericaon (MRV) process.
In the course of Forest Resource Assessment of 2010–2014, a fully funconal RS/GIS laboratory
with trained personnel and hardware, soware and data support, has been established.
Furthermore, not only the capacity of government sta to plan and carry out eld inventory has
been enhanced but DFRS is also now equipped with advanced tools and technology for forest
resource assessment. This instuonal capacity should be connuously enhanced to undertake
periodic forest resource assessment in the future.
6
WAY FORWARD
STATE OF NEPAL’S FORESTS
46
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STATE OF NEPAL’S FORESTS 49
Annex 1: District-wise land cover area (ha)
Development
Region District Forest
(A)
Tree Cover
5–10 %
(B)
Shrubs
(C)
OWL
(B+C)
Other Land
(D)
Total
(A+B+C+D)
Eastern
Bhojpur 72,881 161 676 837 78,957 152,675
Dhankuta 36,779 226 11 237 53,080 90,095
Ilam 93,849 596 75 670 74,582 169,101
Jhapa 17,349 223 135 358 142,662 160,369
Khotang 73,865 109 960 1,069 84,252 159,187
Morang 44,375 132 - 132 137,715 182,221
Okhaldhunga 51,619 587 540 1,127 54,992 107,738
Panchthar 71,872 804 - 804 52,501 125,177
Sankhuwasava 156,102 32,210 3,717 35,926 155,654 347,682
Saptari 20,362 161 613 774 106,954 128,090
Siraha 17,937 224 30 254 95,698 113,890
Solukhumbu 87,144 13,356 10,422 23,778 225,020 335,942
Sunsari 21,719 96 811 907 96,507 119,132
Taplejung 125,010 32,356 246 32,602 206,786 364,398
Terhathum 32,391 695 84 778 34,071 67,240
Udayapur 149,125 871 73 945 79,944 230,013
Total 1,072,379 82,807 18,392 101,198 1,679,374 2,852,952
Central
Bara 45,981 596 51 647 80,639 127,266
Bhaktapur 2,459 15 - 15 9,836 12,311
Chitwan 141,668 5,762 59 5,821 76,481 223,970
Dhading 86,067 5,832 844 6,676 97,932 190,674
Dhanusha 26,975 174 - 174 91,702 118,851
Dolakha 97,091 10,616 135 10,751 107,028 214,871
Kathmandu 15,129 67 83 150 26,082 41,361
Kavrepalanchowk 72,533 2,073 702 2,775 64,135 139,443
Lalitpur 23,924 319 216 536 15,224 39,683
Mahoari 22,189 47 - 47 77,812 100,048
Makwanpur 163,943 1,907 683 2,590 77,833 244,366
Nuwakot 49,423 650 1,966 2,616 67,277 119,317
Parsa 75,843 374 12 387 64,453 140,682
Ramechap 65,248 3,713 412 4,125 87,180 156,553
Rasuwa 49,821 4,735 199 4,935 95,368 150,123
Rautahat 25,874 322 92 414 77,528 103,816
Sarlahi 25,597 175 - 175 100,554 126,326
Sindhuli 165,099 1,048 549 1,598 81,907 248,603
Sindhupalchowk 113,803 1,838 242 2,080 133,142 249,026
Total 1,268,667 40,264 6,248 46,512 1,432,112 2,747,292
STATE OF NEPAL’S FORESTS
50
Development
Region District Forest
(A)
Tree Cover
5–10 %
(B)
Shrubs
(C)
OWL
(B+C)
Other Land
(D)
Total
(A+B+C+D)
Western
Arghakhanchi 73,142 792 26 818 49,950 123,909
Baglung 89,773 251 2,643 2,894 90,909 183,576
Gorkha 109,300 21,933 877 22,810 232,468 364,578
Gulmi 45,215 310 814 1,124 64,439 110,777
Kapilbastu 59,025 1,778 166 1,944 104,167 165,136
Kaski 85,442 7,201 800 8,000 113,250 206,693
Lamjung 86,930 4,568 557 5,125 74,181 166,236
Manang 17,394 9,721 771 10,493 204,152 232,038
Mustang 11,767 14,536 2,189 16,725 327,878 356,370
Myagdi 80,233 22,286 5,888 28,174 120,073 228,480
Nawalparasi 103,593 1,100 713 1,813 109,849 215,255
Palpa 77,974 639 4,160 4,799 63,418 146,191
Parbat 26,454 869 327 1,196 26,506 54,156
Rupandehi 25,105 372 31 403 105,013 130,522
Syangja 46,516 417 1,051 1,468 55,764 103,749
Tanahu 82,200 344 508 852 74,134 157,186
Total 1,020,063 87,117 21,522 108,639 1,816,150 2,944,852
Mid-Western
Banke 116,360 663 886 1,549 70,137 188,046
Bardiya 111,550 1,696 441 2,137 86,378 200,065
Dailekh 73,033 1,382 3,741 5,123 70,402 148,559
Dang 192,682 7,143 900 8,043 105,261 305,986
Dolpa 71,560 45,689 4,363 50,052 672,865 794,477
Humla 81,717 45,631 313 45,944 473,562 601,223
Jajarkot 119,074 6,550 8,852 15,401 87,861 222,336
Jumla 92,841 16,414 12,076 28,490 134,187 255,518
Kalikot 96,075 7,136 12,579 19,715 48,342 164,133
Mugu 76,742 42,177 757 42,934 203,754 323,431
Pyuthan 64,235 347 84 431 67,427 132,092
Rolpa 94,447 731 4,420 5,151 88,951 188,549
Rukum 108,631 15,843 6,973 22,817 158,127 289,575
Salyan 121,258 541 3,257 3,798 63,058 188,114
Surkhet 170,517 2,496 1,563 4,059 74,281 248,857
Total 1,590,722 194,439 61,204 255,643 2,404,594 4,250,959
Far-Western
Achham 98,664 9,514 733 10,248 61,359 170,271
Baitadi 86,581 5,088 1,507 6,595 56,458 149,634
Bajhang 115,312 39,260 788 40,048 191,074 346,433
Bajura 94,430 39,529 1,275 40,804 94,804 230,037
Dadeldhura 111,312 647 1,350 1,997 37,303 150,613
Darchula 78,956 27,918 873 28,791 126,714 234,461
Do 149,083 1,645 2,270 3,916 52,465 205,463
Kailali 198,239 2,093 241 2,334 128,143 328,716
Kanchanpur 77,630 745 422 1,167 82,942 161,740
Total 1,010,206 126,439 9,461 135,900 831,261 1,977,367
Grand Total 5,962,038 531,066 116,826 647,892 8,163,492 14,773,423
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