In recent years, there is no doubt that global climate change (CC) has observable development impacts, which seriously threatens the ability of individuals and communities at all levels. During this process, the clear degradation in the situation of ecosystems has produced a global concern of the urgency to mitigate climate threats and related effects. Assessing the impacts and vulnerability of CC requires accurate, up-to-date and improved information. Coupled with the ready availability of historical remote sensing (RS) data, the reduction in data cost and increased resolution from satellite platforms, RS technology appears poised to make a great impact on planning agencies and providing better understanding the dynamics of the climate system, predict and mitigate the expected global changes and the effects on human civilization involved in mapping Land Use Land Cover (LU/LC) at a variety of spatial scales. This research was designed to study the impact of CC in conflict zones and potential flashpoints in Sudan namely Nuba Mountains, where the community in this area living in fragile and unstable conditions, which making them more vulnerable to the risk of violent conflict and CC effects. And to determine the factors that exacerbate vulnerability in the study area as well as to map and assess the LU/LC change during the period 1984 to 2011 covered the years (1999, 2002 and 2009). Multispectral satellite data (i.e. LANDSAT TM and TERRA ASTER) were used. Change detection techniques were applied to analyze the rate of changes, causal factors as well as the drivers of changes. Recent study showed the importance of spatial variables in tackling CC which promoted the use of maps made within a RS. In addition to provide an input for climate models; and thus plan adaptation strategies.
Nowadays, remote-sensing technologies are becoming increasingly interlinked to the issue of deforestation. They offer a systematized and objective strategy to document, understand and simulate the deforestation process and its associated causes. In this context, the main goal of this study, conducted in the Blue Nile region of Sudan, in which most of the natural habitats were dramatically destroyed, was to develop spatial methodologies to assess the deforestation dynamics and its associated factors. To achieve that, optical multispectral satellite scenes (i.e., ASTER and LANDSAT) integrated with field survey in addition to multiple data sources were used for the analyses. Spatiotemporal Object Based Image Analysis (STOBIA) was applied to assess the change dynamics within the period of study. Broadly, the above mentioned analyses include; Object Based (OB) classifications, post-classification change detection, data fusion, information extraction and spatial analysis. Hierarchical multi-scale segmentation thresholds were applied and each class was delimited with semantic meanings by a set of rules associated with membership functions. Consequently, the fused multi-temporal data were introduced to create detailed objects of change classes from the input LU/LC classes. The dynamic changes were quantified and spatially located as well as the spatial and contextual relations from adjacent areas were analyzed. The main finding of the present study is that, the forest areas were drastically decreased, while the agrarian structure in conversion of forest into agricultural fields and grassland was the main force of deforestation. In contrast, the capability of the area to recover was clearly observed. The study concludes with a brief assessment of an 'oriented' framework, focused on the alarming areas where serious dynamics are located and where urgent plans and interventions are most critical, guided with potential solutions based on the identified driving forces.
The GEOBIA (GEOgraphic Object-Based Image Analysis) approach persists to reveal its effectiveness in remote sensing data analysis, which provides paradigms that integrate analyst’s expert knowledge to generate semantically meaningful image-segments. These segments might contribute to the reduction of problems associated with the analysis of the discrete spectral value of a pixel, such as illumination and shaded tree crowns. However, the challenge in this paper is to introduce a GEOBIA as a sophisticated framework toward the automation of forest structural attributes estimate. Optical sensor was examined to develop models for the estimation of forest attributes. Analyses were performed over a forested selected site in the Blue Nile region of Sudan. The framework of the present research involved; segment extraction, field sample selection, forest attributes generalization, model validation, and mapping the predicted attributes. The rationale for incorporating these data is to offer a semi-automatic GEOBIA approach from which forest attribute is acquired through automated segmentation algorithms at the delineated tree crowns or clusters of crowns level. Correlation and regression analyses were applied to identify the relation between a wide range of spectral, textural, contextual metrics, and the field derived forest attributes. Forest structural attribute estimation results acquired from our GEOBIA framework reveal strong relationships and precise estimates. Furthermore, the best fitted models were cross-validated with independent set of field samples, which revealed a high degree of precision.