How Trend of Increasing Data Volume Affects the Energy Efficiency of 5G Networks

As the rapid growth of mobile users and Internet-of-Everything devices will continue in the upcoming decade, more and more network capacity will be needed to accommodate such a constant increase in data volumes (DVs). To satisfy such a vast DV increase, the implementation of the fifth-generation (5G) and future sixth-generation (6G) mobile networks will be based on heterogeneous networks (HetNets) composed of macro base stations (BSs) dedicated to ensuring basic signal coverage and capacity, and small BSs dedicated to satisfying capacity for increased DVs at locations of traffic hotspots. An approach that can accommodate constantly increasing DVs is based on adding additional capacity in the network through the deployment of new BSs as DV increases. Such an approach represents an implementation challenge to mobile network operators (MNOs), which is reflected in the increased power consumption of the radio access part of the mobile network and degradation of network energy efficiency (EE). In this study, the impact of the expected increase of DVs through the 2020s on the EE of the 5G radio access network (RAN) was analyzed by using standardized data and coverage EE metrics. An analysis was performed for five different macro and small 5G BS implementation and operation scenarios and for rural, urban, dense-urban and indoor-hotspot device density classes (areas). The results of analyses reveal a strong influence of increasing DV trends on standardized data and coverage EE metrics of 5G HetNets. For every device density class characterized with increased DVs, we here elaborate on the process of achieving the best and worse combination of data and coverage EE metrics for each of the analyzed 5G BSs deployment and operation approaches. This elaboration is further extended on the analyses of the impact of 5G RAN instant power consumption and 5G RAN yearly energy consumption on values of standardized EE metrics. The presented analyses can serve as a reference in the selection of the most appropriate 5G BS deployment and operation approach, which will simultaneously ensure the transfer of permanently increasing DVs in a specific device density class and the highest possible levels of data and coverage EE metrics.

Vegetation density analysis using normalized difference vegetation index in East Jakarta, Indonesia

East Jakarta, which is included in the DKI Jakarta Province, continues to grow in population. As a result, the demand for settlement land increases. The presence of plants is critical for environmental equilibrium. The purpose of this study was to determine the vegetation density and its variations in East Jakarta year 2020. The method used the Normalized Difference Vegetation Index (NDVI) analysis and classification. In 2020, the highest NDVI value in East Jakarta was 0.1–0.2, covering 7,952.64 ha (43.07 %) of the entire area, while the lowest value was >0.6, covering 0.06 ha of the total area. The highest vegetation density class in East Jakarta was low dense class, accounting for 7,951.26 ha (43.06 percent) of the whole area, while the lowest density class was under high dense class accounted for 1,116.41 ha (6.04 percent) of the total area. In terms of green open space, there were a city park, a cemetery, a green lane on a road, and a river bank. The municipal park was classified as dense, while the rest were classified as medium dense. The presence of trees within the green space has aided in the area’s vegetation density. It also refers to the role of open green space in enhancing the community’s life and environment’s quality. The importance of educating and guiding the surrounding community about the benefits of vegetation or green open space, then replanting less vegetated land, as well as an integrated land use planning and implementation.The first section in your paper

Vegetation distribution analysis of green open space in Binjai Timur, North Sumatera Province using normalized difference vegetation index

As the city grows, more and more vegetated land is converted to non-vegetated land. This also occurred at Binjai, a city in the North Sumatera Province, Indonesia. The aim of this study was to examine the urban vegetation cover and its changes between 2015 and 2019. The research was carried out in Binjai Timur, which is one of Binjai’s sub-districts. The distribution of vegetation density was measured using the Normalized Difference Vegetation Index (NDVI) value classification. The decrease in the dense class to 10.08 percent was the most significant change in vegetation density class between 2015 and 2019. This was followed by an 8.87 percent increase in the high-density class. This indicates that there is an area with vegetation density increased from lower density to high density. The district has green open spaces in the form of a neighborhood park, cemetery, sub-district park, greenbelt along the road and river, and house yards, according to the field check. These green open spaces were located in low and medium-density areas. The findings suggest that planting trees in those locations and arrangement of vegetation within parks could improve its quality and function. For good quality of urban environments, it is optimizing the use of house yards as vegetated land and boosting green open space quality is required.

ANALISIS VEGETASI DI TAMAN WISATA ALAM (TWA) SUNGAI DUMAI, RIAU

TWA Sungai Dumai is a tourist forest area and ensuring the preservation of natural potential. However, there are problems that can disrupt the sustainability of it, including forest and land fires and conversion of land use to agriculture and oil palm plantations. Until now, there is no vegetation analysis using satellite imagery in TWA Sungai Dumai, so it is important to do so that can be managed sustainably. This study classification of vegetation density classes which are presented in the form of a vegetation density class map in it. This research uses Landsat-8 OLI / TIRS images from October 2017 and October 2020 which are processed to determine density class using Normalized Difference Vegetation Index algorithm. The vegetation density class with the highest area in 2017 was the vegetation density class (2380,832 ha or 66,819% of the total area), while the lowest area was the non-vegetation class (75,737 ha or 2,126% of the total area). The vegetation density class with the highest area in 2020 in TWA Sungai Dumai is dense vegetation density class (3205,039 ha or 89,950% of the total area), while the lowest area is non-vegetation class (1,637 ha or 0.046% of the total area)

Reclassifying the Wildland–Urban Interface Using Fire Occurrences for the United States

The wildland–urban interface (WUI) occurs at the intersection of houses and undeveloped wildlands, where fire is a safety concern for communities, motivating investment in planning, protection, and risk mitigation. Because there is no operational definition of WUI based on where fires in fact have occurred, I used fire occurrences to objectively establish a definition of WUI, while examining spatiotemporal changes, for the conterminous United States. I applied four classifiers, but focused on C5.0, which produced equivalent sensitivity (0.87 to 0.91 at prevalence = 0.67) and generated a ruleset that indicated housing density was the preferable basis for definitions. Fire occurrences overall were predicted for housing densities <100 houses/km2 with potentially low (≥10%) thresholds for percent vegetation cover, varying by housing densities and models. A generalized guideline according to classifications is continued use of existing definitions for wildlands of <6.17 houses/km2 and a low-density intermix class of 6.17 to 50 houses/km2. Departing from other definitions, the medium-density class encompasses 50 to 100 houses/km2 and the high-density class is 100 to 200 houses/km2. Interface, or suburban, communities are 200 to 400 houses/km2. Implications of refining the definition include a larger critical area classified as greater fire risk (low and medium-density WUI below 100 houses/km2) at 855,000 km2 during 2010, and; therefore, incorporation of more communities and homeowners into a high-risk status. The low-density class had greatest risk of fire exposure, but the medium-density class contained a greater concentration of houses. Classification of the wildland–urban interface or intermix based on realized fire occurrences provides an objective foundation for identifying residential densities at risk of fire exposure, which permits disclosure of risk, prioritization of resources to communities and homeowners with greater wildfire exposure, development of strategies for communities to coexist with fire, and responses to reduce vulnerability.

Spatial Monitoring of Urban Development Direction using Landsat 7 ETM+ and Landsat 8 OLI/TIRS in Balikpapan City, Indonesia

The development of urban areas in the city of Balikpapan increases over time and is characterized by increasing population. The growth and development of urban areas needs to be monitored so that the control function on area spatial can be implemented. This research aims to determine the direction of urban areas and measure the density of the built-up as a leading indicator of the development of urban areas in Balikpapan. The method used in this study is the multispasio-temporal analysis of remote sensing data of Landsat 7 ETM+ and Landsat 8 OLI/TIRS which contain a combination of spectral transformation, classification supervised Maximum Likelihood, accuracy assessment and statistical analysis. The results showed the trend of urban development from 2001 to 2019 towards east and northeast with the highest built-up density located in the sub-district of Balikpapan Tengah by 82.07% and followed by the sub-district of Balikpapan Kota by 76.94%. The largest land conversion took place on the bare soil with low vegetation density class to be vegetation with the converted area of 7095.91 ha or approximately 14.10% followed by the bare soil with low vegetation density class to be built-up with the converted area of 5826.86 ha or about 11.58% of the total area of Balikpapan city during the period from 2001 to 2019. The accuracy of urban development map in 2001 reaches 92.39 % and the year 2019 reaches 95.69 %, while the accuracy of land cover map in 2001 reaches 85.57% and the year 2019 reaches 87.28 %.

Spatial Distribution of Potential Area for Community Forest Development in Grindulu Watershed

The effect of deforestation on environmental degradation shifted the orientation of forest management into carrying capacity of the watershed. Based on Law No. 41/1999 on Forestry, mandates adequacy forest area defined a minimum of 30% of the watershed area which fulfilled by public forest and private forest. State forest area has limitations, so the development of community forests is needs for optimal forest area in a watershed is required. The purpose of this study was to determine the spatial distribution of potential areas for community forest development in Grindulu Watershed. The potential of community forest was examined through an interpretation of Landsat 8 of 2016 Path/Row 119/668 for land availability and the transformation of NDVI (Normalized Difference Vegetation Index) as the density classifier. The classification of forest density was low density class of 5148.12 hectares or 7.20% (NDVI = 0 to 0.356), moderate density class of 12,076.39 hectares or 16.88% (NDVI = 0.356 to 0.590), and high density class of 54,294.04 ha or 75.92% (NDVI = 0.590 to 0.841). The land available for prioritized community forest development was 37,774.40 hectares (52.82%) in the form of dry-fields, shrubs, grasses, farms, which were located outside the protected areas and production forest. Based on the assessment of field surveys which were conducted proportionally at 89 samples, known good accuracy results by 0.84. Potential area for community forest development was 31,281.54 ha (43.74%) including in Pacitan (9 districts) of 29,111.98 hectares, Ponorogo (5 districts) of 263.29 hectares, and Wonogiri (2 districts) of 1,906.27 hectares.

Penggunaan Data Citra Landsat Multitemporal untuk Monitoring Kondisi Ekosistem Mangrove di Teluk Kulisusu Kabupaten Buton Utara

Scientific information mangrove ecosystem at Kulisusu bay was limited. This study aims to identify mangrove ecosystems area change at Kulisusu bay within 20 years periods (1995-2015) using satellite imagery Landsat 5 TM (1995), Landsat 7 ETM (2005) and Landsat 8 OLI (2015). Analysis of the mangrove ecosystem changes was used NDVI algorithm then carried classification canopy density by three classes, high, medium and low density.The changes of mangrove ecosystems were based on the area changes of each 10 year. The NDVI results shown that on 1995 to 2005, high and medium classes were reduced respectively by 340 ha and 36 ha, low class area was 172 ha. The period within 2005 to 2015 shows that high areas were reduced by 756 hectares, then medium and low density class 22 Ha and 680 ha respectively. Conditions mangrove ecosystem during 30 years was from 1995 to 2015 shown that had been a heavy reduction in the high density class about 1096 hectares, a reduction of medium density class was 14 hectares and low class was 852 Ha. The amount of reduction in high density class was caused by the mangrove logging activities for charcoal used as raw materials, and fisherman activities likes boat lines for crab fishing ground in the mangrove ecosystem. Keywords: mangrove, NDVI, GIS, Kulisusu Bay

Effect of management practice and age on increment in Pinus patula plantations in Nepal

With the large-scale plantation commenced in the early 1980s, nearly 370,000 hectares of plantations have been successfully established in Nepal. More than 26 thousand hectares (ha) of plantations have been established since late seventies in Sindhupalchok and Kavrepalanchok districts and are handed over to communities as community forests. Pinus roxburghii and Pinus patula are the dominant species of these plantations aiming to maximize biomass productions and restore greenery in degraded hills. The growth rate Pinus patula was estimated 15 m³ ha-1 yr-1 in 1995 which but reduced to 7 m³ ha-1 yr-1 in 2011. As P. patula is an exotic species to Nepal, knowledge on effect of age and management practices on increment was limited in Nepal as well as in the regions. This is hindering in implementations of appropriate silviculture by the forest managers. To fill this knowledge gap, primary data were collected taking sample cores from 120 trees in 2015 from four community forests of Chaubas ridge of Kavrepalanchok district for dendrochronological assessment. Among these four community forests, two followed improved management practices and two followed conventional management practices. To substantiate the data, secondary data of similar studies were used. Dendrochronological assessment taking sample cores of 120 and 80 were conducted in 2000 and 2005 respectively in plantations, managed by community forest users groups, carried out between 1975 AD and 1990 AD in Chaubas ridge of Kavrepalanchok districts. The study found that the growth rate decreased after 12 years and this rate was bigger in the higher density class. The cumulative increment was higher in the lower density class but was found to have retarded rapidly after 15–17 years of age in the higher density class as well as in the conventionally managed plantations. The study recommends conducting planned thinning from the early age of 10–12 years while the final felling is recommended to be executed at the age of 30±5 years for P. patula to maximize volume production. However, most of the plantations have crossed its rotation age, growth rate has been stagnated and there is slim scope of increment from further thinning. In such case, as natural regeneration of the same species is observed encouraging, the study suggest to keep 10–15 seed trees and harvest the remaining. Banko JanakariA Journal of Forestry Information for Nepal Special Issue No. 4, 2018, Page : 27-37