Almost all countries have imposed large-scale mobility restrictions (or lockdown) to stop the spreading of the novel coronavirus (COVID-19). The mobility restriction has disrupted all types of business; causing a devastating impact on countries' economies; and pushing millions of people into extreme poverty. Scientists have been assessing the impact of COVID-19 lockdown on various fronts but there is limited scholarship in the forestry sector. We navigated the impact of COVID-19 lockdown on the forestry sector by taking Gandaki Province (21,974 km 2 ) of Nepal as a case. Employing semi-structured interviews ( n = 62) with all ten stakeholder groups, literature review and media analysis, our study revealed that the COVID-19 lockdown suspended all types of forestry and ecotourism businesses; obstructed research and monitoring activities; halted capacity development and extension services; impacted forest development work; and increased incidences of illegal logging and poaching and trafficking of wildlife. Because of the complete shutdown of businesses, the forestry sector of Gandaki province lost 9.6 million USD and 3.2 million man-days of employment during the lockdown period. The economic cost of the lockdown was 1.73 million USD for NTFPs traders, 1.26 million USD for ecotourism entrepreneurs, 0.55 million USD for the community forest user groups and 0.24 million USD for the smallholder or private forest owner. We suggested four post-COVID recovery pathways, including sustainable forest management, nature-based tourism, improvement of forest products value chain and community-based natural resource management to bounce back from the loss. As the current pandemic is most likely to derail the Sustainable Development Pathways of several countries, including Nepal and necessitates the need for an immediate response, the finding and recommendation of our study may inform decision-makers to reimage post-pandemic recovery and leverage sustainable development.

Vultures are ecologically important primarily because of their scavenging role in cleaning carcasses of the environment. Because of anthropogenic impacts, the Egyptian vulture (Neophron percnopterus) has suffered catastrophic declines in parts of its range and, thus, information about its global distribution and factors influencing its occurrence within this range are essential for its conservation. To this end, we estimated the global distribution of Egyptian vulture and variables related to this distribution. We used occurrence points (n = 4740) from online data sources and literature, environmental variables related to these sites and Maximum Entropy software to model the distribution of this species and its relationship to environmental variables during the entire year, breeding and overwintering. Out of ~ 49 million km2 study area, the Egyptian vulture had a predicted range of 6,599,508 km2 distributed across three continents: Africa, Asia and Europe. The densest distribution was in Southern Europe, India and Northern Africa and a sparser distribution was around Mid and Western Africa, the Middle East and Afghanistan. Climate was related to the vulture’s most probable range: in particular medium temperature seasonality and low precipitation during the coldest yearly quarter were important variables regardless of the season of observations examined. Conservation of identified habitats and mitigation of anthropogenic impacts to conserve these vultures are recommended for immediate and long-term conservation of the Egyptian vulture globally.

A deep neural network (DNN), evolved from a traditional artificial neural network, has been seamlessly adapted for the spatial data domain over the years. Deep learning (DL) has been widely applied for a number of applications and a variety of thematic domains. This article reports on a systematic review of methods adapted in major DNN applications with remote sensing data published between 2010 and 2020 aiming to understand the major application area, a framework for model development and the prospect of DL application in spatial data analysis. It has been found that image fusion, change detection, scene classification, image segmentation, and feature detection are the most commonly used application areas. Based on the publication in these thematic areas, a generic framework has been devised to guide a model development using DL based on the methods followed in the past. Finally, recent trends and prospects in terms of data, method, and application of deep learning with remote sensing data are discussed. The review finds that while DL-based approaches have the potential to unfold hidden information, they face challenges in selecting the most appropriate data, methods, and model parameterizations which may hinder the performance. The increasing trend of application of DL in the spatial domain is expected to leverage its strength at its optimum to the research frontiers.

No abstract

No abstract