Roxb., commonly known as rosewood, is one of the highly valuable tropical timber species of Nepal. The tree species was widely distributed in the past, however, over-exploitation of natural habitat, deforestation, forest conversion for agriculture, illegal logging and the invasion of alien species resulted in the classification of this species as vulnerable by the IUCN (International Union for Conservation of Nature) category. So, the prediction of habitat suitability and potential distribution of the species is required to develop restoration mechanisms and conservation interventions. In this study, we modelled the suitable habitat of over the entire possible range of Nepal using a Maxent model. We compiled 23 environmental variables (19 bioclimatic, 3 topographic and a vegetative layer), however, only 12 least correlated variables along with 43 spatially representative presence locations were retained for model prediction. We used a receiver operating characteristic (ROC) curve to assess the model’s performance and a Jackknife procedure to evaluate the relative importance of predictor variables. The model was statistically significant with an area under the curve (AUC) value of 0.969. The internal Jackknife test indicated that elevation was the most important variable for the model prediction with 71.3% contribution followed by mean temperature of driest quarter (9.8%). The most (>0.6) suitable habitat for the was 235 484 hectares with large sections of area in two provinces whereas, the western most provinces were not suitable for as per Maxent model. The information presented here can provide a framework for nature conservation planning, monitoring and habitat management of this rare and endangered species.Dalbergia latifolia D. latifolia D. latifolia D. latifolia

The high mountains stretch over 20.4% of Nepal’s land surface with diverse climatic conditions and associated vegetation types. An understanding of tree species and forest structural pattern variations across different climatic regions is crucial for mountain ecology. This study strived to carry out a comparative evaluation of species diversity, main stand variables, and canopy cover of forests with contrasting precipitation conditions in the Annapurna range. Firstly, climate data provided by CHELSA version 1.2, were used to identify distinct precipitation regimes. Lamjung and Mustang were selected as two contrasting precipitation regions, and have average annual precipitation of 2965 mm and 723 mm, respectively. Stratified random sampling was used to study 16 plots, each measuring 500 m² and near the tree line at an elevation range of 3000 to 4000 m across different precipitation conditions. In total, 870 trees were identified and measured. Five hemispherical photos using a fisheye lens were taken in each plot for recording and analyzing canopy cover. Margalef’s index was used to measure species richness, while two diversity indices: the Shannon–Wiener Index and Simpson Index were used for species diversity. Dominant tree species in both study regions were identified through the Important Value Index (IVI). The Wilcoxon rank-sum test was employed to determine the differences in forest structure and composition variables between the two precipitation regimes. In total, 13 species were recorded with broadleaved species predominating in the high precipitation region and coniferous species in the low precipitation region. Higher species richness and species diversity were recorded in the low precipitation region, whereas the main stand variables: basal area and stem density were found to be higher in the high precipitation region. Overall, an inverse J-shaped diameter distribution was found in both precipitation regions signifying uneven-aged forest. A higher proportion of leaning and buttressed trees were recorded in the high precipitation region. However, similar forest canopy cover conditions (>90%) were observed in both study regions. The findings of this research provide a comprehensive narrative of tree species and forest structure across distinct precipitation regimes, which can be crucial to administrators and local people for the sustainable management of resources in this complex region.

In the recent years, object-based image analysis (OBIA) approach has emerged with an attempt to overcome limitations inherited in conventional pixel-based approaches. OBIA was performed using Landsat 8 image to map the forest types in Kapilvastu district of Nepal. Systematic sampling design was adopted to establish sample points in the field, and 70% samples were used for classification and 30% samples for accuracy assessment. Landsat image was pre-processed, and the slope and aspect derived from the ASTER DEM were used as additional predictors for classification. Segmentation was done using eCognition v8.0 with the scale parameter of 20, ratios of 0.1 and 0.9 for shape and color, respectively. Classification and Regression Tree (CART) and nearest neighbor classifier (k-NN) methods were used for object-based classification. The major forest types observed in the district were KS (Acacia catechu/ Dalbergia sissoo), Sal (Shorea robusta) and Tropical Mixed Hardwood. The k-NN classification technique showed higher overall accuracy than the CART method. The classification approach used in this study can also be applied to classify forest types in other districts. Improvement in classification accuracy can be potentially obtained through inclusion of sufficient samples from all classes.Banko JanakariA Journal of Forestry Information for NepalVol. 26, No. 1, Page: 38-44, 2016

Tree taper functions expressed in terms of height and diameter at breast height (BDH) can provide accurate and timely information on current growing stock. Taper equations are very important for calculation of growing stock in forest inventories, however, in the context of Nepal, these are still unavailable at the required level. The study aimed to develop taper equations, so that those can be used to predict the diameter anywhere along the stem, and estimate tree volumes at desired sections. The input data was a destructive sampling method of 81 sample trees of Shorea robusta distributed in ten locations of the far western Terai of Nepal (Kailali and Kanchanpur districts). Using two independent B-Spline and 5th-degree polynomial taper models, the upper stem diameters were imputed. Both models were applied on the whole dataset, irrespective of DBH size, to get common fitted taper models. Later, the same models were tested for three different DBH classes to compare and evaluate the best-fit model. Taper models, developed under the B-spline polynomial model of 3^rd degree, were found to be highly dependent on the tree (DBH) sizes. Thus, better models could be developed by classifying the whole dataset based on different DBH classes. On the other hand, developed models under the 5^th degree polynomial taper model did not exhibit their dependency on the tree (DBH) sizes and a better model was found for the unclassified datasets, i.e. for all DBH sizes.

Utilization of land reclamation area offers the potentiality of increasing greenery as well as providing forest products. This study refers to the identification of the land reclamation areas and potential plantation areas on the Bagmati river-basin in the Terai region of Nepal, and recommends appropriate species for plantation in order to rehabilitate such areas. Multi-temporal Landsat Satellite Images (Landsat 7 and Landsat 8) were acquired for 2002 and 2014. Object-based Image Classification method was used to classify the land cover classes into four broad categories: i) Water, ii) Sand and gravel, iii) Plantation potential (open areas suitable for plantation) and iii) Others (forest, agriculture, built-up areas etc.). The Mean Normalized Difference Water Index (NDWI) values and Mean Brightness values were found to be helpful in identifying the water and sand & gravel areas from the other land cover classes. The overall classification accuracy was 0.97 with a kappa coefficient of 0.89 in the case of the 2014 Image classification. In this study, the land reclamation area referred to the areas occupied by water, sand & gravel on the river-beds that were converted into plantation potential and other classes between 2002 and 2014. Similarly, the potential plantation area referred to the summation of the area of reclaimed land, the area of ‘Others’ class converted into ‘Plantation potential’ class and the area that remained to be plantation potential on the bed of the Bagmati River and its tributaries between 2002 and 2014. Altogether, 4,819.10 ha land was reclaimed in the study area, and a total of 5,395.10 ha land was found to be potential for plantation within the study area.Banko JanakariA Journal of Forestry Information for NepalVol. 26, No. 1, Page:53-59, 2016