Bamboo is one of the noble plant species in Ethiopia. Household (HH) income and construction role of highland bamboo (Oldeania alpina (K. Schum.) Stapleto) stands were assessed at Masha district, Southern Ethiopia. Three peasant associations (PAs), Yepo, Yina and Gada, 7–15 key informants and 68, 46, 31 households, respectively were interviewed about the cost and income of bamboo to compare with woody climbers, honey, and mushroom in 2021. Bamboo was one of the main sources of income in all PAs, at least for fencing or house construction. In Yepo, Yina and Gada bamboo accounts 0.7%, 28.1%, 16.3% of the HH NTFP income, respectively. The local people responded that bamboo constructed houses and fences were durable for 15–30 and 2–10 years, respectively. In constructing a 2.44–4.27 m radius local house in Yepo, Yina and Gada 2.4–6 m³, 4.1–5.82 m³ and 3.1–4.3 m³ bamboo culms were harvested at 15, 20, and 30 years interval, respectively by each HH. Bamboo young shoots were also seasonally used for food. Although bamboo provides multiple uses, like substitute for wood and environmental services, it was facing different problems of deforestation. Therefore, policy attention is highly important for bamboo sustainable utilization.

The resistance of platinum filament on heating to different temperatures have been measured. Measurements showed platinum wire resistivity matching to tabulated values, and therefore can be used to obtain the temperature dependence of conductors used in bolometric measurers of radiation.The results obtained make it possible to createabsolute bolometricmeasurer of continuous power and pulse energy of laser radiation.

Every year, hundreds of fires occur in the forests and rangelands across the world and damage thousands hectare of trees, shrubs, and plants which cause environmental and economic damages. This study aims to establish a real time forest fire alert system for better forest management and monitoring in Golestan Province. In this study, in order to prepare fire hazard maps, the required layers were produced based on fire data in Golestan forests and MODIS sensor data. At first, the natural fire data was divided into two categories of training and test samples randomly. Then, the vegetation moisture stresses and greenness were considered using six indexes of NDVI, MSI, WDVI, OSAVI, GVMI and NDWI in natural fire area of training category on the day before fire occurrence and a long period of 15 years, and the risk threshold of the parameters was considered in addition to selecting the best spectral index of vegetation. Finally, the model output was validated for fire occurrences of the test category. The results showed the possibility of prediction of fire site before occurrence of fire with more than 80 percent accuracy.