PORTRAYAL OF YOUNG ADULTS IN DYSTOPIAN YOUNG ADULT LITERATURE—HUNGER GAMES TRILOGY

Young adults shake things up. They exhibit penchant for disturbing the existing institutional systems because their becoming is tantamount to nonconformity. This paper stemmed from the same understanding of the young adults but the conclusion it draws at is that they disrupt the societal codes of behavior and identify themselves as rebels to defy the conventions that plague growth. Dystopia—a subgenre under sci-ficautions us from the wrongs prevalent at present like gender inequality — the offshoot of gender binaries; and if we do not address the pressing issues now, our world might be accelerating towards an abysmal future. This paper is guided by ‘disturbing the universe’ concept propounded by Roberta S. Trites, ‘Utopian Transformation’ by Bradford et al., and Julia Kristeva’s seminal work on ‘Abjection’ theory.&nbsp

Forest Cover Change Pattern after the Intervention of Community Forestry Management System in the Mid-Hill of Nepal : A Case Study

An account of widespread degradation and deforestation in Nepal has been noticed in various literature sources. Although the contribution of community forests (CF) on the improvement of forest cover and condition in the Mid-hill of Nepal is positive, detailed study to understand the current situation seems important. The study area (Tanahun District) lies in the Gandaki Province of western Nepal. The objective of this study was to estimate the forest cover change over the specified period and to identify factors influencing the change. We used Landsat images from the years 1976, 1991, and 2015 to classify land use and land cover. We considered community perception in addition to the forest cover map to understand the different causes of forest cover change. Forest cover decreased from 1976 to 1991 annually at a rate of 0.96%. After 1991, the forest increased annually at a rate of 0.63%. The overall forest cover in the district regained its original status. Factors related to increasing forest cover were emigration, occupation shift, agroforestry practices, as well as particularly by plantation on barren lands, awareness among forest users, and conservation activities conducted by local inhabitants after the government forest was handed over to community members as a community forest management system.Peer reviewe

Geospatial approach to the risk assessment of climate-induced disasters (drought and erosion) and impacts on out-migration in Nepal

Out-migration is one of the most recognized adaptation practices when dealing with scarce resources and disasters. With the general objective of exploring migration as an impact of climate-induced disasters, our study was conducted in Khaniyapani, in the Sunapati rural municipality of Ramechhap district, Nepal. Disaster prioritization was conducted using the pair-wise ranking method, with results suggesting that drought and soil erosion are the most severe disasters in the study area. The severity maps were prepared using remotely sensed data. A Normalized Difference Drought Index and the Revised Universal Soil Loss Equation were used to produce the drought and erosion severity maps, respectively. Approximately 46.2% of the total study area was found to experience severe droughts, and almost 10% of the area had high soil erosion rates. In total, 100 out of 794 households were interviewed for a semi-structured questionnaire. Drought severity was found to directly impact livelihoods due to a decline in agricultural productivity, a decline in livestock, and drying of water sources. Out of 100 families, 64 practiced seasonal migration. A decline in agricultural productivity and livestock, and water scarcity were identified as the most influencing push factors. Excessive seasonal migration has reduced the resilience of these families. Drought-resistant land, water, and crop management techniques and practices, and alternative income-generating activities should be promoted to curb the seasonal migration.Peer reviewe

When students rally for anti-racism : engaging with racial literacy in higher education

Despite a decade of diversity policy plans, a wave of student rallies has ignited debates across western European university campuses. We observe these debates from a situated call for anti-racism in Belgian higher education institutions, and critically reflect on the gap between diversity policy discourse and calls for anti-racism. The students' initiatives make a plea for racial literacy in the curriculum, to foster a critical awareness on how racial hierarchies have been educated through curricula and institutional processes. Students rethink race as a matter to be (un)learned. This pedagogical question, on racial literacy in the curriculum, is a response to diversity policies often silent about race and institutionalised racisms. Students request a fundamental appeal of knowledgeability in relation to race; diversity policy mostly envisions working on (racial) representation, as doing anti-racist work. This article argues how racial literacy might offer productive ways to bridge the disparities between students' calls for anti-racism and the institutional (depoliticised) vocabulary of diversity. We implement Stuart Hall's critical race theory and Jacques Ranciere's subjectification as key concepts to study and theorise these calls for anti-racism as a racial literacy project. This project can be built around engagement as educational concept. We coin possibilities to deploy education as a forum of engagement and dialogue where global asymmetries such as race, gender and citizenship can be critically addressed

Airborne and spaceborne remote sensing for assessment of forest structural attributes across tropical mosaic landscapes

High-resolution, accurate, and updated forest structure maps are urgently required for the implementation of REDD+, payment of ecosystem services, and other climate change mitigation strategies in tropical countries. The collection of forest inventory data is usually labor intensive and costly, and remote sites can be difficult to access. Remote sensing data, for example airborne laser scanning (ALS), hyperspectral imagery, and Landsat data, complement field-based forest inventories and provide high-resolution, accurate, and spatially explicit data for mapping forest structural attributes. However, issues such as the effect of topography, pulse density, and the single and combined use of various remote sensing data on forest structural attributes prediction warrant further research. The main objective of this thesis was to assess airborne and spaceborne remote sensing techniques for modeling forest structural attributes across a montane forest landscape in the Taita Hills, Kenya. The sub-objectives focused on a) the effect of the topographic normalization of Landsat images on fractional cover (Fcover) prediction, aboveground biomass (AGB), and forest structural heterogeneity modeling using ALS and other remote sensing data and b) the analysis of the maps of forest structural attributes. In Study I, the effect of topographic normalization on ALS-based Fcover modeling was evaluated using common vegetation indices and spectral-temporal metrics based on a Landsat time series (LTS). The results demonstrate that the fit of the Fcover models did not improve after topographic normalization in the case of ratio-based vegetation indices (Normalized Difference Vegetation Index, NDVI; reduced simple ratio, RSR) or tasseled cap (TC) greenness; however, the fit improved in the case of brightness and wetness, particularly in the period of the lowest sun elevation. However, if TC indices are preferred, then topographic normalization using a Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) is recommended. In Study II, field-based AGB estimates are modeled by ALS data and a multiple linear regression. The plot-level AGB was modeled with a coefficient of determination (R2) of 0.88 and a root mean square error (RMSE) of 52.9 Mg ha-1. Furthermore, the determinants for AGB spatial distribution are examined using geospatial data and statistical modeling. The AGB patterns are controlled mainly by mean annual precipitation (MAP), the distribution of croplands, and slope, which collectively explained 69.8% of the AGB variation. Study III investigated whether the fusion of ALS with LTS and hyperspectral data, or stratification of the plots to the forest and non-forest classes, improves AGB modeling. According to the results, the prediction model based on ALS data only provides accurate models even without stratification. However, using ALS and HS data together, and employing an additional forest classification for stratification, improves the model accuracy considerably in the studied landscape. Finally, in Study IV, the potential of single and combined ALS and LTS data in modeling forest structural heterogeneity (the Gini coefficient of tree size) was assessed, and the difference between three forest remnants and forest types is evaluated based on predicted maps. If the LTS metrics were included in the models, then ALS data with lower pulse density yield similar accuracy to more expensive, high pulse-density data. Furthermore, the GC map presents forest structural heterogeneity patterns at the landscape scale a

MACROFUNGAL DIVERSITY IN DIFFERENT VEGETATION COMPOSITIONS IN TEGHARI COMMUNITY FOREST, KAILALI, WEST NEPAL

Macrofungi are high-value forest resources that have functionally significant roles in the forest ecosystem. The macrofungal community of three different vegetation compositions, i.e., Sal (Shorea robusta) Forest, Tropical Deciduous Riverine Forest, and Tropical Evergreen Forest of Teghari Community Forest were investigated. Systematic random sampling was made where 60 plots (10 x 10 m) were laid in all different forest types (20 plots in each). A total of 102 macrofungi species were reported belonging to 36 families. Polyporaceae (17 species) was the largest family followed by Tricholomataceae (13 species) and saprophytic fungi were more frequent than mycorrhizal and parasitic fungi. The tropical evergreen forest was rich in macrofungi (59 species) followed by sal forest (40 species) and tropical deciduous riverine forest (38 species). Macrofungal diversity was directly related to surrounding host species. Similarly, increased soil moisture and canopy cover intensified the abundance of saprophytic fungi. The species richness was increased with increasing organic carbon, canopy, moisture, pH, and litter cover. However, soil nitrogen, phosphorus, and potassium were less significant in affecting species richness. Also, the disturbance was negatively correlated with the species richness of macrofungi. This study highlights the hidden diversity which is necessary for the conservation of macrofungi, to optimize forest ecosystem integrity and resilience against biotic and abiotic agent

A method for predicting large-area missing observations in Landsat time series using spectral-temporal metrics

Combined with increasing computing ability, the free and open access to Landsat archive has enabled the changes on the Earth’s surface to be monitored for almost 50 years. However, due to missing observations that result from clouds, cloud shadows, and scan line corrector failure, the Landsat data record is neither a continuous nor consistent time series. We present a new gap-filling method, Missing Observation Prediction based on Spectral-Temporal Metrics (MOPSTM), which uses spectral-temporal metrics computed from Landsat one-year time series and the k-Nearest Neighbor (k-NN) regression. Herein, we demonstrate the performance of MOPSTM by using five, nearly cloud-free, full scene Landsat images from Kenya, Finland, Germany, the USA, and China. Cloud masks from the images with extensive cloud cover were used to simulate large-area gaps, with the highest value we tested being 92% of missing data. The gap-filling accuracy was assessed quantitatively considering all five sites and different land use/land cover types, and the MOPSTM algorithm performed better than the spectral angle-mapper based spatiotemporal similarity (SAMSTS) gap-filling algorithm. The mean RMSE values of MOPSTM were 0.010, 0.012, 0.025, 0.012, and 0.018 for the five sites, while those of SAMSTS were 0.011, 0.017, 0.038, 0.014, and 0.023, respectively. Furthermore, MOPSTM had mean coefficient of determination (R2) values of 0.90, 0.86, 0.78, 0.92, and 0.89, which were higher than those for SAMSTS (0.84, 0.75, 0.55, 0.89, and 0.83). The performance of MOPSTM was not considerably affected by image gap sizes as images ranging from gap sizes of 51% of the image all the way to 92% of the image yielded similar gap-filling accuracy. Also, MOPSTM does not require local parametertuning except for the k values in the k-NN regression, and it can make a gap-free image from any acquisition date. MOPSTM provides a new spectral-temporal approach to generate the gap-free imagery for typical Landsat applications, such as land use, land cover, and forest monitoring.Peer reviewe

Impact of Preprocessing on Tree Canopy Cover Modelling : Does Gap-Filling of Landsat Time Series Improve Modelling Accuracy?

Preprocessing of Landsat images is a double-edged sword, transforming the raw data into a useful format but potentially introducing unwanted values with unnecessary steps. Through recovering missing data of satellite images in time series analysis, gap-filling is an important, highly developed, preprocessing procedure, but its necessity and effects in numerous Landsat applications, such as tree canopy cover (TCC) modelling, are rarely examined. We address this barrier by providing a quantitative comparison of TCC modelling using predictor variables derived from Landsat time series that included gap-filling versus those that did not include gap-filling and evaluating the effects that gap-filling has on modelling TCC. With 1-year Landsat time series from a tropical region located in Taita Hills, Kenya, and a reference TCC map in 0–100 scales derived from airborne laser scanning data, we designed comparable random forest modelling experiments to address the following questions: 1) Does gap-filling improve TCC modelling based on time series predictor variables including the seasonal composites (SC), spectral-temporal metrics (STMs), and harmonic regression (HR) coefficients? 2) What is the difference in TCC modelling between using gap-filled pixels and using valid (actual or cloud-free) pixels? Two gap-filling methods, one temporal-based method (Steffen spline interpolation) and one hybrid method (MOPSTM) have been examined. We show that gap-filled predictors derived from the Landsat time series delivered better performance on average than non-gap-filled predictors with the average of median RMSE values for Steffen-filled and MOPSTM-filled SC’s being 17.09 and 16.57 respectively, while for non-gap-filled predictors, it was 17.21. MOPSTM-filled SC is 3.7% better than non-gap-filled SC on RMSE, and Steffen-filled SC is 0.7% better than non-gap-filled SC on RMSE. The positive effects of gap-filling may be reduced when there are sufficient high-quality valid observations to generate a seasonal composite. The single-date experiment suggests that gap-filled data (e.g. RMSE of 16.99, 17.71, 16.24, and 17.85 with 100% gap-filled pixels as training and test datasets for four seasons) may deliver no worse performance than valid data (e.g. RMSE of 15.46, 17.07, 16.31, and 18.14 with 100% valid pixels as training and test datasets for four seasons). Thus, we conclude that gap-filling has a positive effect on the accuracy of TCC modelling, which justifies its inclusion in image preprocessing workflows.Peer reviewe

Habitat suitability modeling of Asian Elephant Elephas maximus (Mammalia: Proboscidea: Elephantidae) in Parsa National Park, Nepal and its buffer zone

Asian Elephants Elephas maximus in Nepal are known to have habitats and movement corridors in Parsa National Park (PNP) and its buffer zone (BZ), located east of Chitwan National Park. A study was conducted in this area to assess the suitability of PNP and BZ as elephant use areas, and to determine factors relevant to the presence of elephants in PNP. Field measurements were carried out in 67 plots for vegetation analysis. Boosted Regression Tree (BRT) analysis was used to examine the relationship of habitat suitability and variables including topography (slope, aspect, altitude), climate (precipitation, temperature), habitat preference, ground cover and crown cover. The results indicate that elephant habitat suitability is mainly determined by the dominant plant species, temperature, altitude, habitat preference and precipitation. Slope, ground cover, crown cover and substrate have lesser effects. Elephants were recorded up to 400m in the northeast and southeast aspects of the study area. Most suitable habitats were low slope forest dominated by Acacia catechu and Myrsine semicerate that received 300mm annual precipitation. The model emphasizes environmental suitability, and contributes to knowledge for conservation of elephants in PNP and BZ by delineating sites that require specific planning and management.Peer reviewe