Shogufa Popal is currently an Assistant Professor at the Department of Forestry and Natural Resources, Faculty of Agriculture, Kabul University. She obtained her bachelor’s degree from the same department and faculty in 2012. She holds a master’s degree from Laboratory of Global Forest Environmental Studies, Graduate School of Agricultural and Life Sciences, The University of Tokyo in 2018. She did her research on "Object-based Forest Cover Change Mapping using Remote Sensing in Nuristan Province, Afghanistan". Shogufa Popal also has the experience of working with Wildlife Conservation Society and United Nations Environment Programme in Afghanistan.

Deforestation and forest degradation are among significant environmental issues in Afghanistan but has not been studied intensively due to insecurity, confined budget, lack of expertise, and limited accessibility to new technology. In such a situation, remote sensing technology offers practical and economical means to acquire reliable, consistent, and up-to-date information for assessing forest cover and monitoring its spatial and temporal dynamics. Therefore, this study aims to map forest cover changes and to quantify forest loss and gain between 1998 and 2016 in six districts of Nuristan Province in Afghanistan using medium resolution Landsat series data (Landsat TM and Landsat OLI). Our methodology comprises of (i) preprocessing of Landsat images using TNTmips (ii) object-based image classification using eCognition Developer 9, (iii) mapping forest cover change, and (iv) quantifying forest cover loss and land cover dynamics. Altogether, 13 main classes were assigned in the land cover maps with the help of hyperspectral SPOT-7 images and the free web mapping services (Google Earth/Maps and Bing Maps), whereas 3 additional change classes were added in the forest cover change maps(i) forest loss, (ii) forest gain, and (iii) seasonal snow cover. The results shown that although deforestation in six districts of Nuristan Province has not occurred in a large scale, the forests have been continuously degraded between 1998 and 2016. However, we failed to detect the settlement area precisely due to the inadequate resolution of Landsat imageries in identification of smaller areas, rugged topographical features, and characteristics of the settlements. Meanwhile, the performed accuracy assessments on the final land cover and forest cover change maps showed that the utility of high-resolution images for choosing training samples in Landsat images resulted in relatively high overall accuracies (>91 %). Overall, this study can be utilized as a baseline data of forest cover and its spatial and temporal dynamics of Nuristan Province, where the effort was to exploit the potential of freely available, medium-resolution Landsat data series that might contribute in future forest management, restoration, and conservation at a local, provincial, and national level in Afghanistan.