Czimber Kornel

By Emad H.E. Yasin, Ahmed A.H. Siddig, Eiman E. Deiab, Czimber Kornel, Ahmed Hasoba and Abubakr Osman

Dryland forests are ecologically and socioeconomically important. They contribute to livelihood diversification, food security, animal feed and shelter, and environmental conservation in sub-Saharan Africa, particularly Sudan. Despite their importance, current findings show that multiple ecological, human, socio-economic, and policy factors have damaged these resources. As a result, undesirable consequences have been observed, such as food famine, land and water resource degradation, decline/loss of biodiversity, and contribution to global warming that affect the welfare of humans, plants, animals, and micro-organisms. This chapter briefly reviews the forest degradation in drylands Sudan with emphasis on its common causes, impacts, assessment methods, management intervention efforts, and potential future solutions. Given the current situation, there must be urgent combating efforts to manage Sudan\'s dryland forest resources properly. On the one hand, following prevention measures to essentially deal with the current causes thus prevent any further degradation of forest resources in dryland Sudan. On the other hand, there is an urgent need to address current degradation following appropriate and timely rehabilitation interventions. We also recommend adopting a serious monitoring and evaluation system within these combating efforts by applying the five common indicators for measuring forest degradation: biodiversity, productive functions, carbon storage, forest health, and protective functions.

Part of the book: Mitigating Global Climate Change

By Emad H.E. Yasin, Czimber Kornel and Mohamed Hemida

Forest resources in the arid and semi-arid of Sudan are experiencing significant fluctuations in tree cover and ecological functionality. This study aims to bridge this gap by utilizing multi-temporal Landsat imagery and mapping forest cover change in the Nabag Forest Reserve (NFR) in South Kordofan State, Sudan. For this assessment, two cloud-free images (TM from 2011 and OLI from 2021) were downloaded and analyzed using ArcMap 10.7 and ERDAS 2014 software. Supervised classification techniques were applied, corroborated by GPS point verification and field surveys, to quantify changes in forest cover over the decade. The results revealed that dense forest cover increased from 9% in 2011 to 38.9% in 2021, while light forest cover decreased from 34.4% in 2011 to 30.9% in 2021. Additionally, the area occupied by agriculture and barren land declined from 37.2% and 19.4% in 2011 to 18.7% and 11.5% in 2021, respectively. Rapid shifts were observed in all LULC categories during the study period. The primary causes of deforestation and forest degradation were tree felling, unsustainable grazing practices, and construction activities. These findings are crucial for guiding future forest rehabilitation and creating targeted management plans for the local communities reliant on these forests.

Part of the book: Mitigating Global Climate Change

By Emad H.E. Yasin and Czimber Kornel

Satellite image classification serves a critical function across various applications, from land cover mapping and urban planning to environmental monitoring and disaster management. In recent years, significant advancements in machine learning and computer vision, coupled with increased accessibility to satellite imagery, have driven considerable progress in this field. Classification techniques for satellite imagery can be primarily divided into three key approaches: automatic, manual, and hybrid. Each approach offers unique advantages but also comes with its own set of limitations. While most methodologies gravitate toward automatic techniques, choosing an appropriate method should be a carefully considered decision based on specific needs. This paper provides an exhaustive review of cutting-edge classification algorithms, including Artificial Neural Networks (ANNs), Classification Trees (CTs), and Support Vector Machines (SVMs). It also offers a comparative analysis between these modern methods and traditional techniques, focusing on their respective performance metrics when applied to satellite data. This study examines key factors affecting remote sensing data classification, including classifier parameter adjustments and combining multiple classifiers. It reviews existing literature to enhance feature selection and classifier optimization for better accuracy. However, it also points out the continuous need for research in image processing to improve classification accuracy.

Part of the book: Geographic Information Systems

By Emad H.E. Yasin, Ahmed A.H. Siddig and Czimber Kornel

Tropical forests are biodiversity-rich habitats on the globe because they host diversified flora and fauna species, provide a plethora of ecosystem goods and services to local communities, and sustain numerous ecological functions. The forest resources in Sudan are a vital aspect of the nation’s ecological and economic framework. However, they face threats from agriculture, logging, and degradation, leading to loss of forest cover and reduced biodiversity. This chapter reviews Sudan’s dryland forests, highlighting their importance, forest biodiversity, ecosystem services, environmental degradation, conservation challenges, biodiversity information gap, threats, and the urgent need for sustainable management strategies. It emphasizes the significance of protecting these forests to maintain ecological balance and promote socioeconomic development. Addressing these challenges requires urgent and effective conservation and management efforts to preserve these vital resources.

Part of the book: Sustainable Forest Management