Open Foris Collect Earth: a remote sensing sampling survey of Azerbaijan to support climate change reporting in the land use, land use change, and forestry.

Land use, land use change, and forestry (LULUCF) are critical in climate change mitigation. Producing or collecting activity data for LULUCF is essential in developing national greenhouse gas inventories, national communications, biennial update reports, and nationally determined contributions to meet international commitments under climate change. Collect Earth is a free, publicly accessible software for monitoring dynamics between all land use classes: forestlands, croplands, grasslands, wetlands, settlements, and other lands. Collect Earth supports countries in monitoring the trends in land use and land cover over time by applying a sample-based approach and generating reliable, high-quality, consistent, accurate, transparent, robust, comparable, and complete activity data through augmented visual interpretation for climate change reporting. This article reports forest extent estimates in Azerbaijan, analyzing 7782 0.5-ha sampling units through an augmented visual interpretation of very high spatial and temporal resolution images on the Google Earth platform. The results revealed that in 2016, tree cover existed in 31.9% of total land, equal to 2,751,167 ha and 1,301,188 ha or 15.1% of the total land, with a 5.4% sampling error covered by forests. The estimate is 15 to 25% higher than the previous estimates, equal to 169,418 to 260,888 ha of forest that was never reported in previous studies.

Using Open Foris Collect Earth in Kyrgyzstan to support greenhouse gas inventory in the land use, land use change, and forestry sector.

The Kyrgyz Republic (Kyrgyzstan) is one of the countries most vulnerable to the adverse effects of climate change in Central Asia. The land use, land use change, and forestry (LULUCF) sector is critical in climate change mitigation in Kyrgyzstan and is integral to national greenhouse gas (GHG) inventories. However, consistent, complete, and updated activity data is required for the LULUCF sector to develop a transparent GHG inventory. Collect Earth (CE), developed by the Food and Agriculture Organization of the United Nations (FAO), is a free, user-friendly, and open-source tool for collecting activity data for the LULUCF sector. CE assists countries in developing GHG inventories by providing consistent and complete land representation. This article reports an estimate of land use and land-use change dynamics in Kyrgyzstan, based on analyzing 13,414 1-hectare (ha) sampling units through an augmented visual interpretation approach using satellite imagery at the very high spatial and temporal resolution available through the Google Earth platform. The results show that in 2019, forests covered 1.36 million ha or 6.83% of the total land with a 6.23% uncertainty. This estimate was 5 to 16% higher than previous estimates, detecting an additional 63,024 to 188,164 ha of forestland that had not been reported previously. The new estimates suggest an average increase of 10.4% in the current forestlands of Kyrgyzstan.

In-service training policy during the COVID-19 pandemic: The case of the agents of the farmers, rural people, and nomads social insurance fund.

Introduction: The COVID-19 pandemic has posed the in-service training of agents of the Farmers, Rural People, Nomads Social Insurance Fund (hereafter the Fund) to many problems. In-service training is one of the most effective development factors for organizational goals. This sort of training will increase employees' skills and subsequently improve their job performance. Accordingly, the present research mainly aimed to shed light on the effect of in-service training policy on employees' capabilities and job performance. Methods: The research was conducted among the agents of the Fund in Fars province, Iran (N = 197) out of whom 127 agents were sampled by simple randomization. The research instrument was a standard questionnaire whose face and content validity was confirmed by a panel of experts and its reliability was determined by calculating Cronbach's alpha in a pilot study. The results showed that the indicators used to measure the research variables were acceptably consistent with the factor structure and the theoretical framework of the research. Results and discussion: Based on the findings, in-service training in the Fund during the COVID-19 pandemic has had a positive and significant effect on the agents' capabilities (communication and team-working skills, creativity and problem-solving skills, commitment and responsibility, technical information and knowledge, and technical and practical skills) and job performance at the individual, technical, and general levels. Also, the agents' capabilities have had a positive and significant influence on their job performance. It can be concluded that in-service training can influence the agents' capabilities and job performance and improve organizational performance during the COVID-19 pandemic. Thus, the enhancement of in-service training courses' quantity and quality during the COVID-19 pandemic can influence the job performance of the agents at the individual, general, and technical performance levels.

The extent of forest in dryland biomes.

Dryland biomes cover two-fifths of Earth's land surface, but their forest area is poorly known. Here, we report an estimate of global forest extent in dryland biomes, based on analyzing more than 210,000 0.5-hectare sample plots through a photo-interpretation approach using large databases of satellite imagery at (i) very high spatial resolution and (ii) very high temporal resolution, which are available through the Google Earth platform. We show that in 2015, 1327 million hectares of drylands had more than 10% tree-cover, and 1079 million hectares comprised forest. Our estimate is 40 to 47% higher than previous estimates, corresponding to 467 million hectares of forest that have never been reported before. This increases current estimates of global forest cover by at least 9%.