The intensity of solar radiation (SR) is one of the most important required inputs for the estimation of photovoltaic (PV) power station output. Meanwhile, the efficiency of solar PV systems is affected by meteorological factors such as temperature, dust, precipitation, and snow. Meteorological data from satellites provide a viable way for estimating PV potential due to its advantage in spatial coverage and temporal resolution. This paper presents a new approach to adjust SR data from satellites based on the cloud optical thickness (CLOT) before evaluating the solar PV power (PPV) potential, with the effective efficiency of solar cells computed based on temperature, dust, precipitation, and snow. The objective of this study is to evaluate the over-all spatiotemporal solar PV potential in the Asia Pacific region which will holistically include limiting meteorological factors and identify which factor contributes most significantly to the decrease in solar PV potential in selected cities in the region. First, SR and CLOT data from Advanced Himawari Imager 8 and a SKYNET station were processed to derive the correction factor for solar radiation data. Second, satellite data for temperature (MOD11), precipitation (global satellite mapping of precipitation), dust (MOD04), and snow cover (MOD10) were processed to derive the effective solar PV efficiency. Finally, maps showing the seasonal PV power potential over the Asia Pacific region were generated, with selected cities zoomed in for detailed analysis using mean monthly values from March 2016 to February 2017. The results showed that the maximum theoretical PPV in the region was estimated to be 1.9 GW per 17.5 km2 effective pixel area. Moreover, PPV decreased by maximum values of 180 MW, 550 MW, and 225 MW due to temperature, dust, and snow, respectively. For Beijing, Tokyo, and Jakarta, the major contributor to the decrease in PPV is dust, while Khabarovsk is consistently affected by snow effects. Initial validation of the model shows over- and underestimation of solar PV output compared to the actual values by as high as 30%. However, very high values of coefficient of determination (>0.90) show promising results of the model. The contribution of this study is two-fold: regional-scale assessment of PPV potential and investigation of the collective effect and individual contributions of dust, temperature, and snow to the decrease in PPV potential.
This study investigated the drivers of degradation in Southeast Asian mangroves through multi-source remote sensing data products. The degradation drivers that affect approximately half of this area are unidentified; therefore, naturogenic and anthropogenic impacts on these mangroves were studied. Various global land cover (GLC) products were harmonized and examined to identify major anthropogenic changes affecting mangrove habitats. To investigate the naturogenic factors, the impact of the water balance was evaluated using the Normalized Difference Vegetation Index (NDVI), and evapotranspiration and precipitation data. Vegetation indices’ response in deforested mangrove regions depends significantly on the type of drivers. A trend analysis and break point detection of percentage of tree cover (PTC), percentage of non-tree vegetation (PNTV), and percentage of non-vegetation (PNV) datasets can aid in measuring, estimating, and tracing the drivers of change. The assimilation of GLC products suggests that agriculture and fisheries are the predominant drivers of mangrove degradation. The relationship between water balance and degradation shows that naturogenic drivers have a wider impact than anthropogenic drivers, and degradation in particular regions is likely to be a result of the accumulation of various drivers. In large-scale studies, remote sensing data products could be integrated as a remarkably powerful instrument in assisting evidence-based policy making.
ABSTRACT:The impact of climate change in the Philippines was examined in the country's largest basin-the Cagayan River Basin-by predicting its sediment yield for a long period of time. This was done by integrating the Soil and Water Assessment Tool (SWAT) model, Remote Sensing (RS) and Geographic Information System (GIS). A set of Landsat imageries were processed to include an atmospheric correction and a filling procedure for cloud and cloud-shadow infested pixels was used to maximize each downloaded scene for a subsequent land cover classification using Maximum Likelihood classifier. The Shuttle Radar Topography Mission (SRTM)-DEM was used for the digital elevation model (DEM) requirement of the model while ArcGIS™ provided the platform for the ArcSWAT extension, for storing data and displaying spatial data. The impact of climate change was assessed by varying air surface temperature and amount of precipitation as predicted in the Intergovernmental Panel on Climate Change (IPCC) scenarios. A Nash-Sutcliff efficiency (NSE) > 0.4 and coefficient of determination (R 2 ) > 0.5 for both the calibration and validation of the model showed that SWAT model can realistically simulate the hydrological processes in the study area. The model was then utilized for land cover change and climate change analyses and their influence on sediment yield. Results showed a significant relationship exists among the changes in the climate regime, land cover distributions and sediment yield. Finally, the study suggested land cover distribution that can potentially mitigate the serious negative effects of climate change to a regional watershed's sediment yield.
The introduction of solar photovoltaic (PV) systems in isolated areas which are far from the main grid has provided energy to non-electrified households. Such off-grid technology is very promising in the Asia Pacific region where increase in population and regional development has brought an increase in energy demand. This paper presents a methodology to assess the available supply of energy from solar PV systems and the corresponding demand from non-electrified areas. Non-electrified high population density areas were extracted using global population distribution and nightlight data, while the suitability of installing solar PV systems in those areas were identified based on slope, land cover and estimated solar PV power output. Moreover, the cost and benefits of installation were estimated based on the levelized cost of electricity generation from PV (LCOEPV) and the percentage in the total household budget that can shoulder the said expense. Lastly, this study also proposed a novel and simple method to extract the power transmission lines (TLs) based on global road network and nightlight data used for defining off-grid areas. Results show that there are three general types of electrification trend in the region with only 11 out 28 countries exhibiting the ideal trend of decreasing population living in unlit areas with increasing GDP. This study also generated maps showing the spatial distribution of high potential areas for solar PV installation in Cambodia, North Korea and Myanmar as case studies. To date, the high estimated household income allotted for PV electricity is still experienced in most countries in the region, but these countries also have high initial generated electricity from PV systems. Outputs from this study can provide stakeholders with relevant information on the suitable areas for installations in the region and the expected socio-economic benefits.
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