Monitoring Cloud-prone Complex Landscapes At Multiple Spatial Scales Using Medium And High Resolution Optical Data: A Case Study In Central Africa

Tracking land surface dynamics over cloud-prone areas with complex mountainous terrain and a landscape that is heterogeneous at a scale of approximately 10 m, is an important challenge in the remote sensing of tropical regions in developing nations, due to the small plot sizes. Persistent monitoring of natural resources in these regions at multiple spatial scales requires development of tools to identify emerging land cover transformation due to anthropogenic causes, such as agricultural expansion and climate change. Along with the cloud cover and obstructions by topographic distortions due to steep terrain, there are limitations to the accuracy of monitoring change using available historical satellite imagery, largely due to sparse data access and the lack of high quality ground truth for classifier training. One such complex region is the Lake Kivu region in Central Africa. This work addressed these problems to create an effective process for monitoring the Lake Kivu region located in Central Africa. The Lake Kivu region is a biodiversity hotspot with a complex and heterogeneous landscape and intensive agricultural development, where individual plot sizes are often at the scale of 10m. Procedures were developed that use optical data from satellite and aerial observations at multiple scales to tackle the monitoring challenges. First, a novel processing chain was developed to systematically monitor the spatio-temporal land cover dynamics of this region over the years 1988, 2001, and 2011 using Landsat data, complemented by ancillary data. Topographic compensation was performed on Landsat reflectances to avoid the strong illumination angle impacts and image compositing was used to compensate for frequent cloud cover and thus incomplete annual data availability in the archive. A systematic supervised classification, using the state-of-the-art machine learning classifier Random Forest, was applied to the composite Landsat imagery to obtain land cover thematic maps with overall accuracies of 90% and higher. Subsequent change analysis between these years found extensive conversions of the natural environment as a result of human related activities. The gross forest cover loss for 1988-2001 and 2001- 2011 periods was 216.4 and 130.5 thousand hectares, respectively, signifying significant deforestation in the period of civil war and a relatively stable and lower deforestation rate later, possibly due to conservation and reforestation efforts in the region. The other dominant land cover changes in the region were aggressive subsistence farming and urban expansion displacing natural vegetation and arable lands. Despite limited data availability, this study fills the gap of much needed detailed and updated land cover change information for this biologically important region of Central Africa. While useful on a regional scale, Landsat data can be inadequate for more detailed studies of land cover change. Based on an increasing availability of high resolution imagery and light detection and ranging (LiDAR) data from manned and unmanned aerial platforms (\u3c1m \u3eresolution), a study was performed leading to a novel generic framework for land cover monitoring at fine spatial scales. The approach fuses high spatial resolution aerial imagery and LiDAR data to produce land cover maps with high spatial detail using object-based image analysis techniques. The classification framework was tested for a scene with both natural and cultural features and was found to be more than 90 percent accurate, sufficient for detailed land cover change studies

The Predictive Power of Alternative Volatility Forecasting Models over Multiple Horizons

Master thesis Business Administration - University of Agder 2016This thesis paper examines the forecast accuracy and explanatory power of volatility models over multiple forecast horizon for three asset classes. Forecast horizon ranging from 1 month up to 12 subsequent months are investigated using Naïve, EWMA, GARCH, EGARCH, GJR-GARCH and APARCH model for S&P 500, DJIA, CBOE(^TNX ), CBOE(^FVX), USD/CHF and GBP/CHF. MSE and Predictive Power (�) are used to evaluate the forecast accuracy and predictive ability of the model over increasing horizon. Different distribution assumptions are also included with non-linear GARCH models in an attempt to improve forecast accuracy of the models. The in-sample estimation results revealed increased model fit for all assets considering the non-normal innovation but correspondingly didn’t always comply with out-of-sample forecast accuracy. Non-normal distribution provided best forecast accuracy at short forecast horizons for all asset classes except exchange rates. The result common to asset classes was that forecast accuracy and predictive power of the model are best at short horizon which gradually decreased with increasing forecast horizon. The predictive power suggested the longest forecastable horizon for Stock Indices, Interest Rates and Exchange rates are 4 months, 12 months and 2 months respectively. The results showed EGARCH model performed relatively well compared to other models and was able to increase the forecastable horizon. Further, it was concluded there is no best model for all asset classes over all horizons. The best model is largely dependent upon the type of asset and the horizon of interest

TREND ANALYSIS OF AREA, PRODUCTION, PRODUCTIVITY, AND SUPPLY OF POTATO IN SINDHULI DISTRICT AND NEPAL: A COMPARATIVE STUDY

The study; conducted from January, 2020 to June, 2020; focuses on the comparative study of the area, production, and productivity trend of potatoes over 50 years in Sindhuli district and Nepal and a brief overview on quantity supply to the Kalimati fruits and vegetable market. The time-series data from 1968/69 to 2017/18 of Sindhuli and Nepal along with 6 years supply data (2013/14-2018/19) from different districts to Kalimati market were collected from reliable sources (Ministry of Agriculture and Livestock Development and Kalimati Fruits and Vegetable Market Development Board) and analysis was done using Microsoft Excel. Between 1968/69 and 2017/18, the area under potato cultivation in Nepal and Sindhuli has changed by 573 percent and -46 percent respectively while production increased by 907.6 percent in Nepal and 46 percent in Sindhuli. After 1982 dramatic shift in production was observed in Nepal as there was 7 percent of growth rate while in Sindhuli, the production trend highly fluctuates throughout the period. The average yield was 9.75mt/ha and 8.75mt/ha for Nepal and Sindhuli district. Sindhuli district contributes 1.16 percent of Nepalese potato growing area and 0.91 percent of Nepalese potato production. The trend of quantity supply reveals that during 6 years, Indian potato contributes 58 percent of the total amount that came into Kalimati market, while within-country Kavre has the largest share of 19 percent followed by Kathmandu-6 percent and Dolakha-4 percent. However, the trend of quantity supply of potatoes seems highly fluctuating and the Nepalese market is dominated by Indian imports

Determinants of agriculture biodiversity in Western Terai landscape complex of Nepal

The study explored agriculture biodiversity around protected areas and identified factors affecting diversity of agriculture biodiversity in farming households. The study analyzed the data collected from household survey of about 907 farmers from Western-Terai Landscape Complex of Nepal. Intra-species and inter-species richness and evenness in agriculture landscape were estimated and compared across a spectrum of land-uses. The study identified different social, economic, technological and ecological factors affecting the richness of intra-species and inter-species diversity of agriculture biodiversity using generalized linear regression models. Technology index, information index, food security, animal holding, ethnicity, irrigation facility and land-use were found as major variables affecting agriculture. The results also indicated that buffer zones had higher diversity than other land-uses, indicating positive effects of protected-land on surrounding agriculture biodiversity. Results supported need of coordinated efforts to mainstream agriculture biodiversity conservation with landscape conservation plans and socio-economic developments of the region

Determinants of agriculture biodiversity in Western Terai landscape complex of Nepal

The study explored agriculture biodiversity around protected areas and identified factors affecting diversity of agriculture biodiversity in farming households. The study analyzed the data collected from household survey of about 907 farmers from Western-Terai Landscape Complex of Nepal. Intra-species and inter-species richness and evenness in agriculture landscape were estimated and compared across a spectrum of land-uses. The study identified different social, economic, technological and ecological factors affecting the richness of intra-species and inter-species diversity of agriculture biodiversity using generalized linear regression models. Technology index, information index, food security, animal holding, ethnicity, irrigation facility and land-use were found as major variables affecting agriculture. The results also indicated that buffer zones had higher diversity than other land-uses, indicating positive effects of protected-land on surrounding agriculture biodiversity. Results supported need of coordinated efforts to mainstream agriculture biodiversity conservation with landscape conservation plans and socio-economic developments of the region

Partnerships in mental healthcare service delivery in low-resource settings: developing an innovative network in rural Nepal

Background: Mental illnesses are the largest contributors to the global burden of non-communicable diseases. However, there is extremely limited access to high quality, culturally-sensitive, and contextually-appropriate mental healthcare services. This situation persists despite the availability of interventions with proven efficacy to improve patient outcomes. A partnerships network is necessary for successful program adaptation and implementation. Partnerships network We describe our partnerships network as a case example that addresses challenges in delivering mental healthcare and which can serve as a model for similar settings. Our perspectives are informed from integrating mental healthcare services within a rural public hospital in Nepal. Our approach includes training and supervising generalist health workers by off-site psychiatrists. This is made possible by complementing the strengths and weaknesses of the various groups involved: the public sector, a non-profit organization that provides general healthcare services and one that specializes in mental health, a community advisory board, academic centers in high- and low-income countries, and bicultural professionals from the diaspora community. Conclusions: We propose a partnerships model to assist implementation of promising programs to expand access to mental healthcare in low- resource settings. We describe the success and limitations of our current partners in a mental health program in rural Nepal

Tracking Land Use/Land Cover Dynamics in Cloud Prone Areas Using Moderate Resolution Satellite Data: A Case Study in Central Africa

Tracking land surface dynamics over cloud prone areas with complex mountainous terrain is an important challenge facing the Earth Science community. One such region is the Lake Kivu region in Central Africa. We developed a processing chain to systematically monitor the spatio-temporal land use/land cover dynamics of this region over the years 1988, 2001, and 2011 using Landsat data, complemented by ancillary data. Topographic compensation was performed on Landsat reflectances to avoid the strong illumination angle impacts and image compositing was used to compensate for frequent cloud cover and thus incomplete annual data availability in the archive. A systematic supervised classification was applied to the composite Landsat imagery to obtain land cover thematic maps with overall accuracies of 90% and higher. Subsequent change analysis between these years found extensive conversions of the natural environment as a result of human related activities. The gross forest cover loss for 1988–2001 and 2001–2011 period was 216.4 and 130.5 thousand hectares, respectively, signifying significant deforestation in the period of civil war and a relatively stable and lower deforestation rate later, possibly due to conservation and reforestation efforts in the region. The other dominant land cover changes in the region were aggressive subsistence farming and urban expansion displacing natural vegetation and arable lands. Despite limited data availability, this study fills the gap of much needed detailed and updated land cover change information for this biologically important region of Central Africa. These multi-temporal datasets will be a valuable baseline for land use managers in the region interested in developing ecologically sustainable land management strategies and measuring the impacts of biodiversity conservation efforts

Quantification of Barcode Artifact

Shortly after launch, vertical stripes were found in the 3.9 μm channel images of the Advanced Baseline Imager (ABI) on GOES-18. This has been called “Barcode Artifact” (BA) because it is a non-natural phenomenon similar to the barcode found in supermarkets. While BA was initially discovered and monitored qualitatively and subjectively by human inspection, that is not suitable for examining large number of images to characterize BA’s potential dependence on channel, flight module, background signal intensity, time of the day, season of the year, etc., and in particular to evaluate BA’s response to various mitigation measures. We designed three automated methods to quantify BA objectively, tested their effectiveness in practice, and recommend possible future improvement for GOES-19 and the next generation GeoXO Imager (GXI). Declaimer: The scientific results and conclusions, as well as any views or opinions expressed herein, are those of the authors and do not necessarily reflect those of NOAA or the Department of Commerce