19 Trees are an essential part of our life. Trees can also be found outside of the forest areas. FAO (1998) has defined trees outside forest (TOF) as “the trees on the land that fulfils the requirements of forest and other wood land except that the area is less than 0.5 ha and the canopy is < 10%”. For example, scattered trees in permanent meadows and pastures; permanent tree crops such as fruit trees and coconut; trees in park and gardens, around buildings and in lines along streets, roads, railways, rivers, streams and canals; and trees in shelterbelts of less than 20 m width and 0.5 ha area. TOF comprises all trees ranging from a single discrete individual tree to systematically managed trees (Kleinn, 2000). TOF includes both trees as well as shrubs (Foresta at al., 2013). Bamboo is a part of TOF that is merchantable for house construction (Bhusal & Bashyal, 2020). In some cases, total wood production from TOF is more than that from the forests (Krishnankutty et al., 2008). TOF has become an important source for timber globally but still there are no policies related to management, harvest, transit and marketing of timber from TOF (Ghosh & Sinha, 2018). It plays a significant role in meeting the challenges of resource sustainability, poverty Banko Janakari, Vol 32 No. 2, 2022 Pp 19‒36https://doi.org/10.3126/banko.v32i2.50894 Valuation of timber and firewood of trees outside forest along the urban–rural gradient in Kathmandu valley, Nepal This study aims to analyze diameter class, quality class, wood production potential and timber and firewood values of trees outside forest along the urban-rural gradient in Kathmandu valley of central Nepal. Inventory was performed in 209 randomly selected points. Circular plots of 20 m radius were used for inventory. All trees (height > 1.3 m and DBH ≥ 5 cm) in the plots were identified to species level and their height, DBH & quality class were recorded. In total 6,210 trees (236.35 ha-1) of 150 species belonging to 111 genera and 57 families were recorded. The total merchantable timber volumes of timber class A and B, and total timber volumes were highest in the urban stratum (537.08, 84.88 and 621.96 cu ft ha -1 respectively) followed by rural (442.94, 66.82 and 509.76 cu ft ha -1 respectively) and suburban (250.04, 47.31 and 297.35 cu ft ha -1 respectively) strata. But due to higher merchantable price of tree species recorded in rural stratum, total market value of class A timber was higher in rural stratum (NPR 7,89,871/US$ 6,085), class B timber was higher in urban stratum (NPR 1,08,255/US$ 834), total timber was higher in rural stratum (NPR 8,70,410/US$ 6,706), firewood was higher in urban stratum (NPR 4,88,709/US$ 3,765) and total wood was higher in urban stratum (NPR 12,95,531/US$ 9,981). Cinnamomum camphora was found as tree species with highest market price of total wood value in the study area. The study provides the baseline data of useful timber species through TOF suggesting a need for appropriate timber producing species selection for plantation. Keywords: Diameter class, merchantable timber, quality class, strata, wood B. Shrestha 1*, B. K. Sharma 2, and R. K. P. Yadav 1* Received: 2, August 2022 Revised: 9, November 2022 Accepted: 14, December 2022 Published: 31, December 2022 1 Central Department of Botany, Tribhuvan University, Kathmandu, Nepal; *E-mail: biobabita@gmail.com, rkp.yadav@cdbtu.edu.np 2 Conservation Development Foundation (CODEFUND), Nepal, https://orcid.org/0000-0003-1470-0617 https://orcid.org/0000-0001-7432-8007 https://orcid.org/0000-0001-5787-3134 Banko Janakari, Vol 32 No. 2 20 Shrestha et al. reduction, food security, lessening the pressure on forest resources, conserve farmland, increase agricultural productivity and food supplies (Foresta et al., 2013). Besides, TOF also provide the impetus to the growth of wood-based industries and employment opportunities by increasing the extent of area under forest (FSI, 2003) especially more jobs to rural communities (Asanzi et al., 2014). TOF are prominent features in many landscapes, strata (urban, suburban and rural) and substrata (linear, clumped and scattered tree formations) (Baffetta et al., 2010; DFRS, 2011) and serve a number of ecological and economic functions that might be similar to those of forests in different ways and extent (Kleinn, 2000). Wood includes all timber, industrial wood, firewood and charcoal (Krishnankutty et al., 2008). It is still the most widely used fuel source in the developing countries (FAO, 1999a). A timber readily harvestable is the merchantable timber (Ouattara et al., 2014). According to DFRS (2015), a tree can be classified as: quality class A tree (high quality sound tree) - a live tree which would produce at least one 6 m long saw log; quality class B tree (sound tree) –a live tree which would produce at least one 3 m long saw log; and quality class C tree (cull tree) - a live tree not qualified as class A or class B but would produce fire wood only. Forests resources, particularly the timber, face an uncertain future because of high deforestation rate, rapid population growth, timber rights allocation system, forest fees, poor law enforcement, increasing demand for timber and energy and sawmills being export oriented (Oduro et al., 2014). Generally, single trees in areas with lower density attain a larger diameter at breast height (DBH) and as a consequence, greater volume (Bembenek et al., 2014). Stem volume is an important parameter to estimate the monetary value of timber (Crecente-Campo et al., 2009). Categorization of TOF on the basis of diameter class and timber quality class is necessary for the valuation of the wood (Pompa-García, et al., 2009; Bembenek et al., 2014). Merchantable value of the wood depends on the species (Mejia et al., 2015). Merchantable value gives an idea about the economic importance of a species. The organized tree planting first started in the Malla Era was continued up to the Rana Era (Poudel, 2010). New species like Araucaria araucana, other imported species from Europe along with pines were planted to beautify Kathmandu Valley urban areas and palaces. Later with the introduction of modern urban- environmental planning in the 1960s and 1970s, the Government renovated roads and trails throughout Kathmandu. Again in the 1980s, urban environmental planners introduced a three-line green belt. Trees were also planted along either side of other roads (Poudel, 2010), roadside gardens and traffic islands (Baral and Kurmi, 2005). Many parks were also built during different historical eras. A botanical garden and zoo were also built. Thus, many native as well as exotic tree species were planted. Kathmandu valley with the rapid urban population growth rate of 3.9 % is one of the fastest growing urban agglomerations in South Asia (Muzzini & Aparicio, 2013). It is characterized not only by the rapid population growth rate in the urban core but also by the rapid expansion of urban sprawl in the periphery. Plant communities are sensitive to urban expansion and therefore may serve as indicators for human-induced land use change (Vakhlamova et al., 2014). The rural system usually is rich in natural vegetation (Xiao et al., 2017) with more timber production whereas due to rapid urbanization, TOF formations have increased with less timber production in urban area. Thus tree species selection for afforestation in TOF will help to minimize the demand-supply gap of timber (Shrivastav et al., 2012). TOF are little recognized in forest resources assessments, and it is only recently that TOF started receiving attention from the research community and the general public (Kleinn, 2000). Nepal's annual import of wood and wooden materials exceeds NPR 6 billion (RSS, 2019.). In this context, the study of TOF in terms of timber production would be important (Oli, 2002). Since FY 2004/05, the Department of Forests Research and Survey (now the Forest Research and Training Center), Government of Nepal has started the assessment of TOF at national level (FAO, 2009). But it is limited only to volume Banko Janakari, Vol 32 No. 2 21 Shrestha et al. assessment by diameter class. The assessments of TOF in terms of timber and firewood production along the urban-rural gradient are lacking. In this background, this study aims to analyze diameter class, quality class, wood production potential and timber and firewood values of TOF along the urban-rural gradient in Kathmandu valley of central Nepal. Materials and methods Study area The study was carried out in Kathmandu valley (ca. 66,500 ha in area), which includes three districts namely, Kathmandu, Lalitpur and Bhaktapur of Bagmati Province in the middle hill region of central Nepal (ICIMOD, 2007; Figure 1). This bowl-shaped valley extends between 27°32’13” N to 27°49’10” N latitude and 85°11’31” E to 85°31’38” E longitude. It’s elevation ranges between 1,100–2,700 m a.s.l. (Mishra et al., 2019). It is characterized by sub- tropical vegetation and has a distinct monsoon climate with hot and wet summers and cold and dry winters. The average annual minimum and maximum temperature are 1.6°C in January and 31.9°C in April respectively and the average annual rainfall is 1,509 mm (based on DHM data between 2000-2018). Sampling and data collection A two-phase sampling method was applied (Lister et al., 2011). In first phase, the study area was divided into 500 m x 500 m grids (n = 2800) (Figure 1). A total of 1,046 sites with TOF were identified under urban, suburban and rural stratum categorized on the basis of population and urban development (GoN, 2014). Google Earth image interpretation showed that more sites with TOF were found in urban (440) stratum than in suburban (366) and rural (240) strata. Twenty percent of sites with TOF from three strata [urban Figure 1: Map of the study area. The distribution of the sample points in urban, suburban and rural strata Banko Janakari, Vol 32 No. 2 22 Shrestha et al. (88), suburban (73) and rural (48) strata] were selected randomly for the field survey (Figure 1). In second phase field survey was done. Circular sample plots with 20 m radii (area = 0.13 ha) were used for the survey (FRA/DFRS, 2011). Plant characteristics (height, DBH and quality class) of woody plants (trees and shrubs) with height > 1.3 m and diameter at breast height (DBH) ≥ 5 cm were recorded. DBH was measured at 1.3 m above the ground using diameter tape and the tree height was measured using Suunto clinometer (PM-5/360 PC). Tree quality class was also noted for individual trees. Plants were identified to species level based on herbarium specimen prepared following standard procedure (Bridson & Forman, 1998). The vernacular names of the plant species were also recorded with the help of local people and verified with Sharma (2014). Identification was done by using literatures such as Flora of Kathmandu Valley (Malla et al., 1986), followed by comparison with identified specimens previously deposited at Tribhuvan University Central Herbarium (TUCH), Nepal, scientific names were determined. Press et al. (2000) and Plants of the World Online (https:// powo.science.kew.org/) were followed for plant nomenclature. Species richness of the study area and across the strata were estimated with respect to the area sampled at the study area and each stratum. Average species richness was calculated as the total number of species recorded per plot (Dorji et al., 2014). Growth forms of the plants were based on Sharma (2014). Frequency of the individual tree species were also calculated (Danekhu et al., 2016-18). Data analysis DBH and height of the recorded trees were used. Estimation of above ground biomass of trees The total above ground tree biomass was estimated using allometric equation developed by Petersson et al. (2012). AGTB = 0.0509 ρ D2 H Where, AGTB = aboveground tree biomass (kg), ρ= wood specific gravity (g cm-3), D = tree diameter at breast height (cm), H = tree height (m). Sharma & Pukkala (1990) and Zanne et al. (2009) were used for wood specific gravities of tree species. For the tree/shrub species for which wood specific gravity data were not available, the arithmetic mean of all known tree/shrub species in the study area was used (Brown et al., 1989). Estimation of biomass of merchantable timber and firewood Merchantable weights of log of quality class A tree and quality class B tree were calculated only for trees with DBH ≥30 cm as such trees are regarded as mature trees (DoF, 2004 and Brown et al., 2020). The biomass of merchantable timber of log of class A tree and class B tree were estimated using following allometric equations adapted from Petersson et al. (2012). Merchantable weight of log of class A tree AGTB = 0.0509 * ρ* D2* 6 (kg) Merchantable weight of log of class B tree AGTB= 0.0509 * ρ* D2 * 3 (kg) Where, AGTB = aboveground tree biomass (kg), ρ= wood specific gravity (g cm-3), D = tree diameter at breast height (cm), 6 and 3 are the lengths (m) of merchantable logs of class A and class B trees respectively. The biomass of firewood was calculated for timber yielding trees by subtracting the biomass of merchantable timber from the total AGTB of trees. The total AGTB of trees of class C were also accounted as fire wood biomass. These biomasses were converted into kg ha-1. Economic valuation of merchantable timber and firewood As the merchantable timber are sold in cu ft measurement, biomass of merchantable timber of class A and class B trees in ton ha-1 were converted into volume (cubic feet) by multiplying with 40 Banko Janakari, Vol 32 No. 2 23 Shrestha et al. (Wallis, 1970). Market value of merchantable timber of class A and B were calculated by multiplying the merchantable volumes by the per cu ft market price of timber. Unlike timber, the firewood is sold in per kg measurement. So, the market value of firewood was calculated by multiplying the firewood biomass by per kg market price of firewood. Retail market prices of class A, class B logs and firewood of tree species were collected from the retail depots (n = 4) (Ahmed, 2008). The average market prices were used for valuation. For the tree species for which market prices were not available (both timber and firewood), the average values of market prices of all tree species were used (Bembenek et al., 2014). Market values of timber of class A and class B were added to that of firewood to get the total economic value of individual tree species. All these values were then summed up to get the total economic value of all tree species except the bamboo. As bamboos are sold as culms (Bhusal & Bashyal, 2020), their merchantable values were calculated by multiplying the density per hectare by average market price. All these prices were then summed up to get the total merchantable value of wood in each stratum and the study area. Economic values in NPR were converted into US$ by multiplying with 129.8 (1 US$ = 129.8 NPR, accessed on 11/7/2022) Statistical analysis First the data were standardized. Standardized values are calculated by subtracting the sample mean of each variable from each observation and dividing this difference by the sample standard deviation (Gotelli & Ellison, 2013). Those values were then tested for normality. However, the data were not normal and their normality did not improve even after transformation so Kruskal- Wallis test with post-hoc Mann-Whitney test at p ≤ 0.05 were used for comparison among groups. PAST (V 4.09; Hammer et al., 2001) was used for analysis. Results Plant species diversity A total of 150 species of plants [trees (n=121) and shrubs (n=29)] belonging to 111 genera and 57 families were enumerated from the study area. Though the average species richness was found to be higher in urban stratum than in suburban and rural strata, Kruskal-Wallis test followed by Mann-Whitney test showed that there are no significant differences among the strata (Table 1 and Table 2). Table 1: Species richness of trees outside forest along the urban-rural gradient in Kathmandu valley, Nepal. Number of plots, species richness (range), species richness ha-1 and average species richness (ha-1) in three strata are shown. Different superscript letters indicate statistical significance at p<0.05 (Kruskal-Wallis test followed by Mann Whitney test) Strata Number of plots Species richness (range) Species richness (ha-1) Average species richness (ha-1) Urban 88 109 (1-21) 9.85 55.95±31.67a Suburban 73 89 (1-16) 9.7 46.31±26.99a Rural 48 85 (2-15) 14.09 45.74±23.35a Banko Janakari, Vol 32 No. 2 24 Shrestha et al. Table 2: Scientific name, vernacular name, English name, family and frequency of species of trees outside forest species found in the study area S.N. Scientific name Vernacular name English name Growth form Family Frequency (%) 1 Acacia catechu (L.F.) Willd. Khayar Cutch tree Tree Leguminosae 0.48 2 Acacia nilotica (L.) Willd. ex Del. Babool Gum arabic tree Tree Leguminosae 0.96 3 Acer oblongum Wall. ex DC. Phirphire Himalayan maple Tree Sapindaceae 0.48 4 Agave cantula Roxb. Ketuke Century plant Shrub Agavaceae 0.48 5 Alangium chinense (Lour.) Harms Baman patti Chinese alangium Tree Alangiaceae 0.48 6 Albizia julibrissin Durazz. Rato siris Mimosa tree Tree Leguminosae 9.09 7 Albizia procera (Roxb.) Benth. Seto siris White siris tree Tree Leguminosae 0.96 8 Alnus nepalensis D. Don Uttis Alder Tree Betulaceae 10.05 9 Alstonia neriifolia D. Don Tree Apocynaceae 1.91 10 Alstonia scholaris (L.) R. Br. Chatiwan Devil's tree Tree Apocynaceae 3.35 11 Anthocephalus chinensis (Lam.) A. Rich. ex Walp. Kadamgachi Kadam Tree Rubiaceae 0.48 12 Araucaria bidwillii Hook. Dhengre sallo Monkey puzzle Tree Araucariaceae 6.70 13 Araucaria columnaris J. R. Forst & Hook. Coral reef araucaria Tree Araucariaceae 0.48 14 Araucaria heterophylla (Salisb.) Franco Living Christmas tree Tree Araucariaceae 8.13 15 Areca catechu L. Bhale supari Betel nut Tree Palmae 1.44 16 Artocarpus integra (Thumb.) Merr. Rookh katahar Jack fruit Tree Moraceae 0.48 17 Azadirachta indica A. Juss. Neem Neem tree Tree Meliaceae 2.39 18 Bambusa nepalensis Stapleton Bansa Bamboo Grass Gramineae 3.83 19 Bauhinia variegata L. Koiralo Purple orchid tree Tree Leguminosae 2.87 20 Berberis asiatica Roxb. ex DC. Chutro Barberry Shrub Berberidaceae 0.96 21 Borassus flabellifer L. Taad Toddy palm Tree Palmae 5.74 22 Bougainvillea glabra Choisy Kagaj phool Paper flower Shrub Nyctaginaceae 2.39 23 Brugmansia arborea Pers. Dhaturo Angel's trumplet Shrub Solanaceae 1.91 24 Buchanania latifolia Roxb. Chiraungi Cuddaph almond Tree Anacardiaceae 0.48 25 Buddleja asiatica Lour. Bhimsenpati Butterfly bush Shrub Loganiaceae 8.61 26 Burretiokentia vieillardii Pic. Serm. Taad Tiger palm Tree Palmae 0.48 27 Callistemon citrinus (Curtis) Skeels Kalki phool Bottle brush Tree Myrtaceae 19.14 28 Camellia japonica L. Chinia guransa Garden camellia Shrub Theaceae 0.96 Banko Janakari, Vol 32 No. 2 25 Shrestha et al. S.N. Scientific name Vernacular name English name Growth form Family Frequency (%) 29 Carica papaya L. Mewa Papaya Tree Caricaceae 2.39 30 Carya illinoensis (Wangenheim) K. Koch Picanut Pecan Tree Juglandaceae 0.48 31 Caryota urens L. Jagar Fish- tail palm Tree Palmae 0.96 32 Cassia fistula L. Raj brichya Cassia pods Tree Leguminosae 0.48 33 Cassia mimosaides L. Amala jhar Tooth cup Shrub Leguminosae 0.48 34 Casuarina equisetifolia L. Jangali jhyau Whistling pine Tree Casuarinaceae 0.48 35 Cedrus deodara (Roxb. ex D. Don) G. Don Devdaru Deodar Tree Pinaceae 0.96 36 Celtis australis L. Khari Europian nettle tree Tree Ulmaceae 29.67 37 Cestrum nocturnum L. Rat ki rani Night jasmine Shrub Solanaceae 0.48 38 Choerospondias axillaris (Roxb.) B.L.Burtt & A.W.Hill Lapsi Nepali hog plum Tree Anacardiaceae 11.96 39 Cinnamomum camphora (L.) J. Presl Kapoor Camphor Tree Lauraceae 41.15 40 Cinnamomum tamala (Buch-Ham) Nees & Eberm. Tejpatta Cinnamon leaf Tree Lauraceae 0.48 41 Citrus aurantifolia (Christm) Swingle Kagati Lemon Tree Rutaceae 5.74 42 Citrus jambhiri Lush. Jyamir Florida lemon Tree Rutaceae 0.96 43 Citrus limon (L.) Burn. F. Nibuwa Lime Tree Rutaceae 1.91 44 Citrus maxima (Burm.) Herr. Bhogate Pummelo Tree Rutaceae 18.18 45 Citrus reticulata Blanco. Suntala Mandarin orange Tree Rutaceae 0.48 46 Cotinus coggygria (Scop.) Rato peepal Smoke bush Shrub Anacardiaceae 0.48 47 Croton roxburghii Balakrishnan Ach Croton Tree Euphorbeaceae 0.48 48 Cycus pectinata Buch.- Ham. Kalbal Cycus Tree Cycadaceae 0.96 49 Cyphomandra betaceae (Cav.) Sendt Tyamter Tree tomato Shrub Solanaceae 0.96 50 Dalbergia sissoo Roxb. Sisau Indian rosewood Tree Leguminoceae 4.31 51 Diospyros kaki Thunb. Haluwabed Persimon Tree Ebenaceae 4.31 52 Diploknema butyracea (Roxb.) Lam. Chiuri Butter fruit Tree Sapotaceae 0.48 53 Duranta erecta L. Nil kanda Golden dewdrops Shrub Verbenaceae 0.96 54 Elaeocarpus sphaericus (Gaertn.) K. Schum. Rudrakshya Utrasum bead tree Tree Elaeocarpaceae 5.74 55 Eriobotrya japonica (Thumb.) Lindl. Laukat Loquat Tree Rosaceae 0.48 56 Erythrina arborescens Roxb. Theki kath Himlayan coral bean Tree Leguminosae 0.48 57 Erythrina stricta Roxb. Phaledo Indian coral tree Tree Leguminosae 1.91 58 Eucalyptus camaldulensis Dehn. Masala River red gum Tree Myrtaceae 4.78 59 Euphorbia pulcherrima Willd. Ex Klotzsch Lalupate Poinsettia Shrub Euphorbeaceae 1.91 Banko Janakari, Vol 32 No. 2 26 Shrestha et al. S.N. Scientific name Vernacular name English name Growth form Family Frequency (%) 60 Ficus auriculata Lour. Timilo Roxburgh fig Tree Moraceae 2.39 61 Ficus benghalensis L. Bar Banyan fig Tree Moraceae 5.74 62 Ficus benjamina L. Sami Weeping fig Tree Moraceae 4.78 63 Ficus elastica Roxb. Rubber plant Rubber plant Tree Moraceae 7.18 64 Ficus lacor Buch.-Ham. Kabhro Java fig Tree Moraceae 5.26 65 Ficus neriifolia Sm. Dudhilo Willow leaf fig Tree Moraceae 0.48 66 Ficus religiosa L. Pipal Sacred fig Tree Moraceae 25.84 67 Ficus semicordata Buch- Ham ex Sm. Khanayo Drooping fig Tree Moraceae 0.48 68 Fraxinus floribunda Wall. Lankure Ash Tree Oleaceae 1.44 69 Ginkgo biloba L. Maidenhair tree Tree Ginkgoiaceae 0.48 70 Gossypium arborium L. Kapas Cotton plant Shrub Malvaceae 0.48 71 Grevillea robusta A. Cunn. ex R. Br. Kagiyo Silky oak Tree Proteaceae 29.19 72 Hibiscus brackenridgei A. Gray Rose mallow Shrub Malvaceae 0.48 73 Hibiscus rosa-sinensis L. Ghanti phool China rose Shrub Malvaceae 1.44 74 Homalium napaulense (DC.) Benth. Falame kanda Tree Flacourtiaceae 0.48 75 Ilex excelsa (Wall.) Hook. Fil. Pwanle Tree Aquifoliaceae 1.44 76 Jacaranda mimosifolia D.Don Nilo phool Jacaranda Tree Bignoniaceae 19.14 77 Jasminum mesnyi Hance Double jai Primrose jasmine Shrub Oleaceae 0.48 78 Juglans nigra L. Hade okhar Black walnut Tree Juglandaceae 4.31 79 Juglans regia L. Dante okhar English walnut Tree Juglandaceae 2.87 80 Juniperus chinensis L. Dhupi Chinese juniper Shrub Cupressaceae 0.96 81 Juniperus communis L. Dhupi Pencil cedar Shrub Cupressaceae 0.48 82 Juniperus indica Bertol. Dhupi Black juniper Shrub Cupressaceae 6.22 83 Juniperus recurva Buch.- Ham. ex D. Don Dhupi Himalayan juniper Tree Cupressaceae 3.35 84 Lagerstroemia indica L. Asare phool Crape myrtle Tree Lythraceae 12.44 85 Lagerstroemia parviflora Roxb. Bot dhairo Crepe flower Tree Lythraceae 0.48 86 Lagerstroemia reginae Roxb. Thulo asare Queen's crape myrtle Shrub Lythraceae 0.48 87 Leucaena leucocephala (Lam.) De Wit Epil Ipil ipil Tree Leguminoseae 0.96 88 Ligustrum confusum Decne. Kanike rookh Privet Tree Oleaceae 0.48 89 Lindera pulcherrima (Nees) Benth. ex Hook. F. Shyal phusre Wild privet Tree Lauraceae 0.96 90 Litchi chinensis Sonner Lichi Lychee Tree Santalaceae 0.96 91 Litsea monopetala (Roxb.) Pers. Kutmiro Many- flowered litsea Tree Lauraceae 3.83 92 Macadamia integrifolia Maiden & Betche Queensland nut Tree Proteaceae 0.48 Banko Janakari, Vol 32 No. 2 27 Shrestha et al. S.N. Scientific name Vernacular name English name Growth form Family Frequency (%) 93 Madhuca longofolia (Koeing) Chiuri Macbride Tree Sapotaceae 0.48 94 Magnolia soulangeana Soul. Neel kamal Saucer magnolia Tree Magnoliaceae 0.48 95 Mahonia nepaulensis DC. Jamanemandro Mahonia Tree Berberidaceae 0.48 96 Malvaviscus arboreus Cav. Khursani phool Turkcap Shrub Malvaceae 0.96 97 Mangifera indica L. Aap Mango Tree Anacardiaceae 7.18 98 Manglietia insignis (Wall.) Blume Rookh kamal Tree Magnoliaceae 5.74 99 Melia azedarach L. Bakaino China berry Tree Meliaceae 14.35 100 Michelia champaka L. Champ Champaca Tree Magnoliaceae 4.78 101 Michelia fuscata Bl. Kankakchampa Banana shrub Shrub Magnoliaceae 0.48 102 Miliusa velutina (Dunal) Hook. f. & Thombs Kali kath Velveti miliusa Tree Annonaceae 0.48 103 Morus alba L. Kimbu Common mulberry Tree Moraceae 5.74 104 Murraya koenigii (L.) Sprengel Kadi patta Curry tree Tree Rutaceae 0.48 105 Musa paradisiaca L. Kera Banana Shrub Musaceae 0.96 106 Myrica esculenta Buch- Ham. ex D. Don Kafal Box myrtale Tree Myricaceae 0.96 107 Myrsine capitellata Wall. Seti kath Tree Myrsinaceae 0.48 108 Nerium indicum Miller Karbir Indian oleander Tree Apocynaceae 1.44 109 Nerium oleander variegatum Kannel Kaner Tree Apocynaceae 2.39 110 Nyctanthes arbor-tristis L. Parijat Coral jasmine Tree Oleaceae 7.66 111 Persea americana Mill. Ghiu phal Avocado Tree Lauraceae 8.61 112 Persea duthiei (King ex Hook. F.) Kosterm. Kaulo Duthiei bay tree Tree Lauraceae 2.87 113 Phoenix humilis Royle. Khajur Dwarf date palm Tree Palmae 0.96 114 Phoenix sylvestris Roxb. Taadi Wild date palm Tree Palmae 0.48 115 Phyllanths emblica L. Amala Emblic Tree Euphorbeaceae 2.87 116 Pinus roxburghii Sarg. Khote salla Chir pine Tree Pinaceae 14.35 117 Platanus orientalis L. Chinar Oriental plane Tree Platanaceae 0.48 118 Podocarpus neriifolius D. Don Gunsi Oleander Podocarp Tree Podocarpaceae 0.48 119 Populus jacquemontiana Dode. Lahare pipal Poplar Tree Salicaceae 11.96 120 Prunus avium L. Cherry Sweet cherry Tree Rosaceae 0.48 121 Prunus cerasoides D. Don Paiyun Himalayan cherry Tree Rosaceae 9.57 122 Prunus domestica L. Aaloo bokhada Europian plum Shrub Rosaceae 7.18 123 Prunus persica (L.) Batsch Aaroo Peach Tree Rosaceae 8.61 124 Psidium guajava L. Amba Guava Tree Myrtaceae 17.22 125 Punica granatum L. Anar Pomegranate Tree Punicaceae 4.78 126 Pyrus communis L. Naspati Europian pear Tree Rosaceae 0.48 Banko Janakari, Vol 32 No. 2 28 Shrestha et al. S.N. Scientific name Vernacular name English name Growth form Family Frequency (%) 127 Pyrus crenata Buch.- Ham. ex D. Don. Naspati Wild pear Tree Rosaceae 0.96 128 Pyrus malus L. Syau Apple Tree Rosaceae 0.96 129 Pyrus pashia Buch.- Ham. ex D. Don. Mayal Wild Himalayan pear Tree Rosaceae 5.74 130 Pyrus pyrifolia (Burn.) Nak. Naspati Asian pear Tree Rosaceae 9.57 131 Quercus glauca Thumb. Falant Ring-cupped oak Tree Fagaceae 0.48 132 Rhododendron arboreum Smith Lali guransa Tree rhododendron Tree Ericaceae 0.96 133 Ricinus communis L. Andir Castor bean Shrub Euphorbiaceae 0.48 134 Salix tetrasperma Roxb. Bainsa Indian willow Tree Salicaceae 11.00 135 Sambucus hookeri Rehder Galeni Elder Tree Sambucaceae 6.70 136 Sapindus mukorossi Gaertn. Rittha Soap berry Tree Sapindaceae 0.96 137 Schefflera impressa (C. B. Clarke) Harms Simaal Schefflera vine Tree Araliaceae 1.44 138 Schima wallichii (DC.) Korth. Chilaune Needlewood tree Tree Theaceae 6.22 139 Spathodea campanulata P. Beauv African tulip tree Tree Bignoniaceae 0.48 140 Syzygium cumini (L) Skeels Jamuna Malabar plum Tree Myrtaceae 8.61 141 Syzygium jambos (L.) Alston Gulab jamun Rose apple Tree Myrtaceae 2.87 142 Tecoma stans (L.) H. B. K. Ghata pushpi Yellow bell Shrub Bignoniaceae 0.96 143 Thespesia lampas (Cav.) Dalz. & Gibs. Ban kapas Common mallow Shrub Malvaceae 1.91 144 Thuja orientalis L. Mayur Pankhi Cedar Tree Cupressaceae 29.19 145 Toona ciliata M. Roem. Tooni Indian cedar Tree Meliaceae 0.48 146 Trachycarpus sp. H. Wendl. Taad Fan palm Tree Palmae 0.96 147 Vitex negundo L. Simali Five-leaved chaste tree Shrub Verbenaceae 0.96 148 Woodfordia fruticosa (L.) Kurz Dhangero Fire flame bush Shrub Lythraceae 0.48 149 Zanthoxylum armatum DC. Timur Prickly ash Tree Rutaceae 0.48 150 Ziziphus incurva Roxb. Hade bayar Bead plum Tree Rhamnaceae 1.44 Tree density, tree height and stem DBH The average tree density in the study area was 236.35±173.12 ha-1. Maximum height of the tree was 31.50 m with an average of 6.83±3.77 m. Similarly, maximum DBH of the stem was 203 cm with an average of 21.44±19.49 cm. The average tree density was higher in suburban stratum (248.44±198.56 ha-1) than in urban (232.58±155.08 ha-1) and rural (224.88±165.31 ha-1) strata (Table 3). However, the difference was not significant. The tallest tree (31.50 m) and widest tree (203 cm) were found in urban stratum. The urban stratum was found to have significantly taller and wider trees than suburban and rural strata (Table 3). Banko Janakari, Vol 32 No. 2 29 Shrestha et al. Table 3: Average tree density (±SD), maximum and average (±SD), tree height and maximum and average (±SD) stem DBH of trees outside forest along the urban-rural gradient in Kathmandu valley, Nepal. Different superscript letters indicate statistical significance at p<0.05. Strata Average Tree Density (Number of stem ha-1) Tree Height (m) Stem DBH (cm) Maximum Average Maximum Average Urban 232.58±155.08 a 31.5 7.64±4.63a 203 22.80±19.85a Suburban 248.44±198.56 a 20.3 6.43±2.97bc 157.3 20.36±16.80b Rural 224.88±165.31 a 23 5.92±2.68c 187 20.62±22.59b Tree density by quality class and stem diameter class The average density of merchantable trees of quality class A, quality class B and quality class C in the study are were 20.90±37.01 ha-1, 13.09±22.16 ha-1 and 202.37±178.53 ha-1 respectively. The average density of merchantable trees of quality class A was found to be significantly higher (p<0.05) in urban stratum than in rural stratum but that did not differ significantly from that in suburban stratum (Table 4). Moreover, the differences in average densities of merchantable trees of quality class B and C were not significant among three strata (p<0.05). Similarly, the average stem densities of diameter classes 5-9.90 cm, 10-19.90 cm, 20-29.90 cm and ≥ 30 cm in the study area were found to be 79.51±107.30 ha -1, 64.28±77.09 ha-1, 34.94±36.62 ha-1 and 57.24±60.92 ha-1 respectively (Table 4). There were no significant differences in the average stem densities across different strata except for the diameter class 20-29.90 cm. Urban and suburban strata were found to have significant stem density of diameter class 20-29.90 cm than the rural stratum (Table 4). Table 4: Average densities (±SD) of trees by tree quality classes and stem diameter classes of trees outside forest along the urban-rural gradient in Kathmandu valley, Nepal. Different superscript letters indicate statistical significance at p<0.05. Strata Average density of trees by quality class (ha-1) Average stem density by diameter class (ha-1) (cm) A B C 5-9.90 10-19.90 20-29.90 ≥30 Urban 28.20±45.50a 15.37±23.57 a 189.02±160.72 a 66.26±93.69 a 68.97±71.06 a 37.06±35.84a 60.29±60.12 a Suburban 18.85±29.60ab 11.77±21.87 a 217.83±207.20 a 87.83±119.45 a 61.35±83.17 a 40.10±43.78a 59.17±67.38 a Rural 10.61±25.96b 10.94±19.87 a 203.35±163.62 a 92.80±111.09 a 60.16±79.29 a 23.20±20.75b 48.72±51.80 a Volume and biomass of merchantable timber and firewood Out of 53 tree species with merchantable timber recorded in the study area, 13 species could yield timber of quality class A, 13 could yield timber of quality class B, while 27 could yield timber of both quality class A and B (Appendix I). The total volume of merchantable timber in the study area was 625.51 cu ft ha -1 with 549.33 cu ft ha -1 and 76.18 cu ft ha -1 as volumes of merchantable timber class A and class B. Total biomass of firewood was 50840.85 kg ha-1. The volume of merchantable timber was highest in the urban stratum followed by rural and suburban strata while biomass of firewood was highest in urban stratum followed by suburban and rural strata (Table 5). https://frtc.gov.np/downloadfile/Shrestha%20etall%20Appendix_1672983385.pdf?fbclid=IwAR2eJuN79JCdgh9cvpgdyIMROV-7lFoWZbAPPCCTcPA9s8if3YsSciHKwCQ Banko Janakari, Vol 32 No. 2 30 Shrestha et al. Table 5: Volume of merchantable timber and biomass of firewood from trees outside forest along the urban-rural gradient in Kathmandu valley, Nepal. Volume ha-1 by quality class and total volume ha-1 of merchantable timber and total biomass ha-1 of firewood Strata Class A timber (cu ft ha-1) Class B timber (cu ft ha -1) Total timber (cu ft ha -1) Firewood (kg ha -1) Urban 537.08 84.88 621.96 55835.49 Suburban 250.04 47.31 297.35 39410.01 Rural 442.94 66.82 509.76 39032.12 Market value of merchantable wood The total market values were calculated based on the per unit market price of the timber and firewood in the study area (Appendix II). Based upon the market values of individual tree species. (Appendix III), the total market values of timber class A, timber class B, total timber, firewood and total wood from the TOF were found to be NPR. 746,613 (US$ 5,752), NPR. 96,358 (US$ 742), NPR. 842,971 (US$ 6,494), NPR. 516,612 (US$ 3,980) and NPR. 1362,880 (US$ 10,500) ha-1. The market value of total merchantable timber was highest in the rural stratum followed by urban and suburban strata while that of firewood was highest in urban stratum followed by suburban and rural strata (Appendix IV, Appendix V, Appendix VI and Table 6). Cinnamomum camphora was the tree species with highest market value of timber class A, timber class B, total timber and total wood value ha-1 as (NPR. 229,851) (US$ 1,771), (NPR. 17,399) (US$ 134), (NPR. 247,250) (US$ 1,905) and (NPR. 2,96,101) (US$ 2,281) in the study area (Table 7). Pinus roxburghii was the tree species with highest market value of firewood as (NPR. 63,793) (US$ 491) here. Rural stratum had the highest merchantable values for timber class A in C. camphora, for total timber in C. camphora, for firewood in Eucalyptus camaldulensis and for total wood in C. camphora while the urban stratum had the highest merchantable value for timber class B in C. camphora. Economically, C. camphora, recorded from 86 plots and P. roxburghii recorded from 30 plots showed the highest merchantable timber and firewood values respectively in the study area. 32 timber class A and 49 timber class B logs of C. camphora were estimated from the study sites. C. camphora was second highest expensive species, the retail market prices of which varied from NPR 2200 to 3200. E. camaldulensis, S. cumini and F. floribunda were other tree species with more economic valuations. Local merchantable market prices matter during valuation because they vary for a single species. Table 6: Market values (MV) of merchantable wood i.e., timber plus firewood from trees outside forest along the urban-rural gradient in Kathmandu valley, Nepal. Market values of timber of class A and B, total timber, firewood and total wood in NPR and US$ Strata MV of timber class A (NPR ha-1) US$ MV of timber class B (NPR ha-1) US$ MV of total timber (NPR ha-1) US$ MV of firewood (NPR ha-1) US$ MV of total wood (NPR ha-1) US$ Urban 698,567 5,382 108,255 834 806,821 6,216 488,709 3,765 1,295,531 9,981 Suburban 383,485 2,954 60,759 468 444,244 3,423 444,104 3,421 888,828 6,848 Rural 789,871 6,085 80,539 620 870,410 6,706 380,303 2,930 1,250,713 9,636 https://frtc.gov.np/downloadfile/Shrestha%20etall%20Appendix_1672983385.pdf?fbclid=IwAR2eJuN79JCdgh9cvpgdyIMROV-7lFoWZbAPPCCTcPA9s8if3YsSciHKwCQ https://frtc.gov.np/downloadfile/Shrestha%20etall%20Appendix_1672983385.pdf?fbclid=IwAR2eJuN79JCdgh9cvpgdyIMROV-7lFoWZbAPPCCTcPA9s8if3YsSciHKwCQ Banko Janakari, Vol 32 No. 2 31 Shrestha et al. Table 7: Tree species and their market values (MV) of timber class A, timber class B, total timber, fire wood and the total wood in different strata of the study area Strata Species MV of timber class A (NPR ha-1) US$ Species MV of timber class B (NPR ha-1) US$ Species MV of total timber (NPR ha-1) US$ Species MV of firewood (NPR ha-1) US$ Species MV of total wood (NPR ha-1) US$ Urban Cinnamomum camphora 218,142 1,681 Cinnamomum camphora 31,077 239 Cinnamomum camphora 249,219 1,920 Pinus roxburghii 86,736 668 Cinnamomum camphora 315,156 2,428 Suburban Eucalyptus camaldulensis 116,498 898 Syzigium cumini 16,069 124 Eucalyptus camaldulensis 120,125 925 Pinus roxburghii 91,760 707 Syzigium cumini 175,826 1,355 Rural Cinnamomum camphora 530,006 4,083 Fraxinus floribunda 15,196 117 Cinnamomum camphora 543,055 4,184 Eucalyptus camaldulensis 92,461 712 Cinnamomum camphora 591,656 4,558 Discussion A total of 150 plant species were reported from the study area (Table 2). Vakhlamova et al. (2014) found slightly high species richness (160) in urban–rural gradient in Kazakhstan. It might be due to enumeration of all vascular plants regardless of DBH in the study. Moreover, Thompson (2010) found comparatively less species diversity (22) from in Khartoum, Sudan. It is possibly due to enumeration of only the living fences in the urban and suburban gardens where homogeneity of species occurs. Species richness in terms of stratum area (ha-1) was higher in rural stratum than in urban and suburban strata (Table 1). Vakhlamova et al. (2014) also found an increasing trend of species richness from urban to rural in urban–rural gradient in Kazakhstan, Western Siberia. This pattern can be explained by the fact that plant life forms and evolutionary strategies do not follow any urban-rural gradient, rather are affected by varied habitat and landscape features. In addition, reduced suitable habitats for plants in densely built-up urban areas and excessive trampling of vegetated patches might cause decrease in plant diversity (Aronson et al., 2014). Average species richness (ha-1) was higher in urban stratum than in suburban and rural strata which are due to trees were planted types in the urban stratum while majority of them were natural woodlots in remaining strata. Tree density in the present study area was found higher than that in TOF in Morang (15 ha-1) (DFRS, 2007) and Nawalparasi (10 ha-1) districts (Kharal et al., 2008) which might be due to dominance of agricultural lands and less tree plantation culture in Terai area. The average tree density was found more in suburban stratum than in urban and rural strata which is due to abundance of trees with 20-29.9 stem diameter class indicating more branched trees here. The higher average tree density in urban stratum than in rural in this study (Table 3) showed the similar patterns in Morang and Nawalparasi districts (DFRS, 2007; Kharal et al., 2008) This pattern could be due to plantation drives (also includes exotic species) during Panchayat regime in the urban areas in Kathmandu valley and major other urban areas (Goutam, 2018). Moreover, rural people cut down the trees for domestic use. The average trees heights and average DBH also followed the same distribution pattern (DFRS, 2007 and Kharal et al., 2008). Average density of tree quality class A and B were found more in urban stratum than in suburban and rural strata whereas that of tree quality class C was found more in rural stratum than in suburban and urban strata (Table 4). This is supported by the occurrence of more average stem density and distribution of mature trees (≥30) in the urban stratum. This is due to more abundance of such mature trees Eucalyptus camaldulensis, Ficus elastica, Jacaranda mimosifolia etc.) in the parks, roads, river and stream lines etc. whereas due to less dominance of such sized trees, rural stratum had less average tree density with dominance of smaller stem diameter class. Furthermore, both tallest tree and widest tree were also recorded in urban stratum. Out of four stem diameter classes, dominance of smaller diameter class (5-9.90 cm and 10-19.90 cm) in the urban stratum in the study area (Table 4) is similar as Morgenroth et al. (2020) reported in America’s urban forests as > 40% of trees in Banko Janakari, Vol 32 No. 2 32 Shrestha et al. the smallest DBH class (< 15 cm) which could be attributed to preference for smaller ornamental trees as Bottle brush, Albizia, Junipers etc. in urban areas or a recent increase in tree planting efforts. A greater proportion of stem diameter classes of 10-19.9 cm and ≥ 30 cm in urban stratum were also same as Morgenroth et al. (2020) found the dominance of 16–45 cm DBH class in urban forests. This may be due to existence of youthful trees. As regards the stem densities of diameter classes 5-9.90 cm, 10-19.90 cm, 20-29.90 cm and ≥ 30 cm in Kathmandu valley, values are higher than those reported from Morang (DFRS, 2007) and Nawalparasi (Kharal et al., 2008); that could be attributed to less planted trees in both Morang district and Nawalparasi district. Further, stem density of lower diameter class (5-9.90 cm) was higher in rural stratum than that in suburban and urban strata whereas stem densities of higher diameters were higher in urban and suburban strata except for trees of diameter 20-29.9 cm which showed uneven distribution. This result is consistent with the findings reported from Morang district (DFRS, 2007), but different from that reported from Nawalparasi district (Kharal et al., 2008). This could be due to more naturally regenerated trees in rural stratum in both Morang and the study area. Also, tree plantation drive earlier during Rana regime and Panchayat regime would have contributed to this pattern of tree size class distribution (Goutam, 2018). TOF are important in terms of wood production. DFRS (2015), on the basis of FAO recommendation, has stated that 13.29% of middle mountains forests have the potential of timber production. This study shows slightly higher value (14.38%) of timber production by TOF. Similar results are found in India (FSI, 2011; Ghosh & Sinha, 2018) as well as in Kerala, India (Krishnankutty et al., 2008). But Yadav et al. (2020) reported higher percentage of timber production (25.17%) from TOF in Dhangadhi Municipality, Siraha district, Nepal which is due to more distribution of planted tree species with wider DBH there. In a study by Bembenek et al. (2014), high mean tree height, mean DBH and high mean volume of merchantable timber with low mean tree density of Scots pine were reported. The higher volumes of merchantable timbers of class A and class B in the urban stratum than in rural and suburban strata in the study area could be due to distribution of more mature and taller trees. It might be due to conservation of the old trees in the parks, road sides, river lines, pond lines, etc. Similarly, lower volumes of merchantable timbers of class A and class B in the rural stratum might be due to lesser tree density as well as less dominance of stem density of ≥ 30 diameter class. Conclusions Urban TOF are important in terms of species diversity whereas suburban TOF are richer in terms of density. Due to the presence of large sized tree species planted during Rana regime, urban TOF have taller and wider trees. Due to more tree density of timber class A and class B in urban stratum, volumes of total merchantable timber along with timber class A and class B and biomass of merchantable firewood were also found higher here. Rural TOF are economically more important. Due to high market prices of the wood of tree species recorded in rural stratum, market value of timber class A and total timber were found higher in rural stratum than the others. Urban TOF are also economically important because it showed high market value for B class timber, firewood and total wood. In terms of TOF species, C. camphora and P. roxburghii were found to be economically more important as they showed the highest merchantable timber and firewood values. People should be encouraged for afforestation in TOF areas with these species which offers opportunity of timber availability and could help in local livelihood. On the other hand, import of wood and wooden materials could be minimized as well as urban greenery would be enhanced. Acknowledgements Mr. Mahendra Shrestha, Mr. Mayukh Shrestha, Mrs. Laxmi Joshi Shrestha, Mrs. Prativa Neupane and local people are acknowledged for their support during data collection. Mr. Mayukh Shrestha helped in developing sampling location points map of the study area. Banko Janakari, Vol 32 No. 2 33 Shrestha et al. References Ahmed, P. (2008). Trees Outside Forests (TOF): A case study of wood production and consumption in Haryana. International Forestry, 10(2), 165–172. Aronson, M. F., La Sorte, F. A., Nilon, C. H., Katti, M., Goddard, M. A., Lepczyk, C. A., et al. (2014). A global analysis of the impact of urbanization on bird and plant diversity reveals key anthropogenic drivers. Proceedings of the Royal Society, Series B.281, 1–8. Asanzi, P., Putzel, L., Gumbo, D., & Mupeta, M. (2014). Rural livelihoods and the Chinese timber trade in Zambia's western province. International Forestry Review,16(4), 447- 458. URL: https://doi.org/10.1505/146554 814813484095. Baffetta, F., Corona, P., & Fattorini, L. (2010). Assessing the attributes of scattered trees outside the forest by a multi-phase sampling strategy. Oxford Journals, Life Sciences, Forestry, 84(3), 315–325. Baral, R., B., & Kurmi P., P. (2005). Assessing city beutification with plants: The Kathmandu perspective. Banko Jankari, 15(1), 49–57. Bembenek, M., Karaszewski, Z., Kondracki, K., Lacka, A., Mederski, P. S., Skorupski, M., Strzelinski, P., Sulkowski, S., &Wegiel. A. (2014). Value of merchantable timber in Scots pine stands of different densities. Drewno, 57(192), 133–142. DOI: 10.12841/wood.1644–3985.S14.09. Bhusal, S., & Bashyal, S. (2020). Assessing the status, utilization and market value chain of Bamboo species (Case study from Chure area of Arghakhanchi District, Nepal). Global Scientific Journals,8(7), 2254–2275. ISSN 2320–9186. www. globalscientificjournal.com Bridson, D., & Forman, L. (1998). The Herberium Hand Book, 3rd Edition, Royal Botanical Garden, UK. Brown, S., Gillespie, A. J. R., & Lugo, A., E. (1989). Biomass estimation for tropical forests with applications to forest inventory data. Forest Science, 35(4), 881–902. Brown, H. C. A., Berninger, F. A., Larjavaara, M., & Appiah, M. (2020). Above-ground carbon stocks and timber value of old timber plantations, secondary and primary forests in Southern Ghana. Forest Ecology and Management, 472,1-11. 118236.https:// doi.org/10.1016/j.foreco.2020.118236 Crecente-Campo, F., Alboreca, A. R. & Di-eguez- Aranda, U. (2009). A merchantable volume system for Pinus sylvestris L. in the major mountain ranges of Spain. Ann. For. Sci., 66(8), 808-808. c_ INRA, EDP Sciences, DOI: 10.1051/forest/2009078. www.afs- journal.org. Danekhu, U., Shrestha, R., & Maharjan, S., R. (2016-18). Assessment of non timber forest products in Baghmara Buffer Zone Community Forest, Chitwan, Nepal. Journal of Natural History Museum, 30, 209–220 DFRS (2007). Tree Outside Forests (Morang and Dhanusa). Government of Nepal. DFRS (2011). Field manual for Assessment of Trees outside Forests (TOF). FRA-Nepal. DFRS (2015). Nepal State of Nepal’s forests. ISBN: 978-9937-8896-3-6 www.dfrs. gov.npDFRS (2015). Middle Mountains Forests of Nepal. FRA-Nepal. ISBN:978- 9937-8896-2-9. http://frtc.gov.np Dida, J. J. V., Quinton, K R. F., & Bantanyan, N. C. (2016). Assessment of Trees Outside Forests as Potential Food Source in Second District, Makati City, Philippines. Ecosystems & Development, 6(1), 10–14. ISSN 2012–3612. DoF (2004). Community Forestry Inventory Guidelines. (in Nepali) Department of Forests, Babarmahal, Kathmandu, Nepal. Banko Janakari, Vol 32 No. 2 34 Shrestha et al. Dorji, T., Moe, S. R., Klein, J. A., & Totland, O. (2014). Plant Species Richness, Evenness, and Composition along Environmental Gradients in an Alpine Meadow Grazing Ecosystem in Central Tibet, China. Arctic, Antarctic and Alpine Research, 46(2), 308– 326. FAO (1998). Trees Outside the Forests (TOF). Conservation, Research and Education Service (FORC), FAO, Rome. Retrieved from www.fao.org/docrep/003/X6685E/ X6685E07.htm. FAO. (1999a). State of the World's Forests. Rome, 154 pp. FAO (2009). Asia-Pacific Forestry Sector Outlook Study II. Working Paper No. APFSOS II/ WP/2009/05 Foresta, H. d., Somarriba, E., Temu, A., Gauthier, Boulanger. D., Feuilly, H., & Gauthier, M. (2013). Towards the Assessment of Trees Outside Forests. FRA working Paper 183. FAO. Rome. FSI (2003). State of forest report 2003, Forest Survey of India, Ministry of Environment and Forests, Dehradun, India. FSI (2011). India State of Forest Report. https://www.fsi.nic.in/forest-report-2011 Accessed on 12/27/2020 Ghosh, M., & Sinha, B. (2018). Policy analysis for realizing the potential of timber production from trees outside forests (TOF) in India. International Forestry Review, 20(1), 89- 103.https://doi.org/10.1505/14655481882 2824255. GoN, DHM. Government of Nepal. Department of Hydrology and Meteorology, Kathmandu, Nepal. GoN. (2014). Population Monograph of Nepal. National Planning Commission Secretariat. Central Bureau of Statistics, Kathmandu, Nepal. 1. ISBN: 978-9937-2-8971-9. Gotelli, N.J., & Ellison, A.M. (2013). A Primer of Ecological Statistics. Sinauer Associates, Inc. Goutam, K. R. (2018). Urban forestry in the federal context of Nepal. Banko Janakari, 28(1), 1–2. Hammer Ø, Harper DAT, & Ryan PD. (2001). PAST: Paleontological Statistics Software Packagefor Education and Data Analysis. Palaeontol Electron, 4(1), 9 pp. ICIMOD (2007). Kathmandu Valley Environment Outlook. ISBN 978 92 9115 019 9. 978 92 9115 020 5 (electronic). Kharal, D. K., Giri, R. K., & Karna, D. L. (2008). Assessment of Trees Outside Forests Nawalparasi District, Nepal. Department of Forest Research and Survey, Kathmandu, Nepal. Kleinn, C. (2000). On Large-area inventory and assessment of trees outside forests. Unasylva, 51(200), 3–10. Klimas, C. A., Kainer, K. A., & Wadt, L. H. de O. (2012). The economic value of sustainable seed and timber harvests of multi-use species: An example using Carapa guianensis. Forest Ecology and Management, 268, 81–91. www.elsevier. com/locate/foreco Krishnankutty, C. N., Thampi, K. B., & Chundamannil, M. (2008). Trees Outside Forests (TOF): A case study of the wood production and consumption situation in Kerala. International Forestry Review,10(2), 156–64. doi:10.1505/ ifor.10.2.156. Malla, S. B., Rajbhandari, S. B., Shrestha, T. B., Adhikari, P. M. and Adhikari, S. R. (ed.). 1986. Flora of Kathmandu Valley. Ministry of Forests and Soil Conservation. Department of Medicinal Plants, Kathmandu, Nepal. Mejia, E., Pacheco, P., Muzo, A., & Torres, B. Banko Janakari, Vol 32 No. 2 35 Shrestha et al. (2015). Smallholders and timber extraction in the Ecuadorian Amazon: Amidst market opportunities and regulatory constraints. International Forestry Review, 17(1). URL: https://doi.org/10.1505/1465548. 15814668954. Mishra, B., Sandifer, J., & Gyawali, B. R. (2019). Urban heat island in Kathmandu, Nepal: Evaluating relationship between NDVI and LST from 2000 to 2018. International Journal of Environment, 8(1), 17–27. ISSN 2091-2854. DOI: http://dx.doi. org/10.3126/ije.v8i1.22546 Morgenroth, J., Nowak, D. J., & Koeser, A. K. (2020). DBH distributions in America’s urban forests-An overview of structural diversity. Forests, 11(135). doi:10.3390/ f11020135. www.mdpi.com/journal/ forests. Muzzini, E., & Aparicio, G. (2013). Urban Growth and Spatial Transition in Nepal-An Initial Assessment. The World Bank. ISBN 978-0-8213-9659-9 — ISBN 978-0-8213- 9661-2 (electronic). DOI: 10.1596/978-0- 8213-9659-9 Oli, B. N. 2002. Trees outside forests: An ignored dimension of forest resource assessment. Banko Janakari, 12 (1), 79–81. Oduro, K.A., Arts, B., Hoogstra-Klein, M.A., Kyereh, B. & Mohren, G.M.J. (2014). Exploring the Future of Timber Resources in the High Forest Zone of Ghana. International Forestry Review, 16(6), 573- 585. URL: https://doi.org/10.1505/146554 814814095320. Ouattara, A., N'Da, H. D., Hervé, M., A., B., & Fernand, K. (2014). Assessment of the Merchantable Timber Volume of the Savannah Woodlands on the Communal Lands in Northeastern Côte d'Ivoire. International Journal of Scientific & Engineering Research, 5 (12), ISSN 2229– 5518 Petersson, H., Holm, S., Stahl, G., Alger, D., Fridman, J., Lehtonen, A., Lundstrom, A., & Makippa, R. (2012). Individual tree biomass equations or biomass expansion factors for assessment of carbon stock changes in living biomass- A comparative study. Forest Ecology and Management, 270, 78–84. Pompa-García, M. J., Corral-Rivas, J., Hernández- Díaz, J. C., & Alvarez-González, J. G. (2009) A system for calculating the merchantable volume of oak trees in the northwest of the state of Chihuahua, Mexico. Journal of Forestry Research, 20(4), 293–300. DOI 10.1007/s11676-009- 0051-x Poudel, K. (2010). Green streets: the trees of Kathmandu. http://ecs.com.np/features/ green-streets-the-trees-of-kathmandu. Assessed on 8/24/2019 Press, J. R., Shrestha, K. K., Sutton, D.A. (2000). Annotated Checklist of the Flowering Plants of Nepal. The Natural History Museum, London. RSS (2019). Country’s dependency on wood increasing, timber import exceeds Rs 6 billion. My Republica. [accessed 2022 Dec 24]. http://myrepublica.nagariknetwor k.com/news/70395/. Sharma, B. K. (2014). Bioresources of Nepal. Subidhya Sharma, Nepal. Sharma, E. R., & Pukkala, T. (1990). Volume equations and biomass prediction of forest trees of Nepal. Forest Survey and Statistics Division, Ministry of Forest and Soil Conservation. Kathmandu, Nepal. 47. Shrivastav, A., Pandey, A. K., & Dubey R. (2012) Assessment of important trees outside forests (TOF) in Gorakhpur District of Uttar Pradesh. The Indian Forester, 138(3),252- 256. DOI: 10.36808/if/2012/v138i3/4622 Banko Janakari, Vol 32 No. 2 36 Shrestha et al. Tang, Y. J., Chen, A. P., & Zhao, S. Q. (2016). Carbon Storage and Sequestration of Urban Street Trees in Beijing, China. Frontiers in Ecology and Evolution, 4(53), 1–8. doi: 10.3389/fevo.2016.00053 Thompson, J. L., Gebauer, J., & Buerkert, A. (2010). Fences in urban and peri-urban gardens of Khartoum, Sudan. Forests, Trees and Livelihoods, 19, 379-391. DOI: 10.1080/14728028.2010.9752679 Vakhlamova, T., Rusterholz, H. P., Kanibolotskaya, Y.,& Baur, B. (2014). Changes in plant diversity along an urban– rural gradient in an expanding city in Kazakhstan, Western Siberia. Landscape and Urban Planning,132, 111-120. www. elsevier.com/locate/landurbplan Wallis, N. K. (1970). Australian Timber Handbook.Angus & Robertson Ltd., 221 George Street, Sydney Xiao, L., He, Z., Wang, Y. & Guo, Q. (2017) Understanding urban– rural linkages from an ecological perspective Sustainable Development & World Ecology, 24(1), 37–43. DOI: 10.1080/13504509.2016.1157105 Yadav, Y., Chhetri, B. B. K., Raymajhi, S., Tiwari, K. R., & Sitaula, B. K. (2020). Evaluating contribution of trees outside forests for income of rural livelihoods of Terai region of Nepal. Open Journal of Forestry,10(4), 388-400. https://doi. org/10.4236/ojf.2020.104024 Zanne, A. E., Lopez-Gonzalez, G., Coomes, D. A., Jansen, I. J., Jansen, S., Lewis, S. L., Miller, R. B., Swenson, N. G., Wiemann, M. C., & Chave, J. (2009). Global wood density database. Dryad. Identifier: http:// hdl.handle.net/10255/dryad.235.