Abdelhameed assessment, show that in 2001 the forest converges

Abdelhameed LisanDepartment of Geoinformatics – Z_GIS, University of Salzburg, Schillerstraße 30, Salzburg, Austria([email protected])AbstractThe impacts of armed conflict on ecosystems are complex and difficult to assess due to restricted access to affected areas during wartime making satellite remote sensing a useful tool for studying direct and indirect effects of conflict on the landscape. This study aims to identify forest cover changes in Jebel Marra region western Sudan for 2001 and 2017 using Object-Based Image Analysis (OBIA) applying change detection technique. Two Landsat imagery used for case study one form 2001 which is prior conflict and the other from 2017 post conflict. The Image has been preprocessed to enhance spatial resolution for the imagery. Pre-classification change detection technique has been applied. Two data sets acquired at different dates were analyzed together to detect locations where change has occurred. Change assessment, show that in 2001 the forest converges area was 485.9 km2, and in 2017 was 319.85 km2, it decreased by about 267km2 compared to 2001 Also it reveals that forest increased in some parts by about 101.33km2 in 2017 compared to  2001 and the  unchanged area about 218.5km2.The study demonstrates that there is a significant decrease in the forest coverage due to the direct and indirect effect of the armed conflict Also it reveals that there is a small increase of the forest in some part, however, half of the total area changed compared to 2001.1. IntroductionThe direct effects of war on civilians are generally well-understood and have been extensively documented (Clodfelter, 2002; Ismael,2007; Keegan, 1994; Sidel & Levy, 2008; Tardanico, 2008). The indirect effects of war due to the use of munitions on a nation’s land, air and water can also have adverse, long-term and far-reaching effects on human populations and the surrounding environment (Joksimovich,2000). Often overlooked is the effect of war on land cover, which in turn impacts biodiversity (Dudley & Woodford, 2002), even though 90% of the major armed conflicts between 1950 and 2000 occurred within countries containing biodiversity hotspots and more than 80% took place directly within the hotspots (Hanson et al., 2009). Several studies have concluded that armed conflict is generally deleterious to plants and animal’s due to habitat destruction and fragmentation, direct loss of animals from poaching or land mines, over-exploitation (Gorsevski, Virginia et al 2012). forest cover maps derived from satellite images play a key role in global, regional, national and subnational level conservation. Forest cover map is available in a range of data formats and spatial resolutions to suit different user requirements. According to the study area size and details of mapping selection suitable satellite image is important. Presently satellite imagery became widely available when affordable (Kabir Uddin 2014).Since the advent of satellite based Earth observation, land cover change detection has been a major driver of developments in the analysis of remotely sensed data (Anuta and Bauer 1973, Anderson 1977, Nelson 1983, Singh 1989, Lu et al. 2004, Aplin 2004, Coppin et al. 2004). More recently, high spatial resolution imagery has been available from commercial operators providing unique opportunities for detailed characterization and monitoring of forest ecosystems (Wulder et al. 2004, 2008c, Hay et al. 2005, Falkowski et al. 2009), urban areas (Herold et al. 2002, Hay et al. 2010) and additional applications developed to address increasingly detailed information needs (Castilla et al. 2008, Chen et al. 2011). Land cover change refers to variations in the state or type of physical materials on the Earth’s surface, such as forests, grass, water, etc., which can be directly observed using remote sensing techniques (Fisher et al. 2005). Remote sensing detection, defined by Singh (1989) as “the process of identifying differences in the state of an object or phenomenon by observing it at different times”, provides a means to study and understand ecosystems’ patterns and processes at a range of geographical and temporal scales. While knowledge of land cover conditions at a given point in time is important, the dynamics or trends related to specific change conditions offer unique and often important insights, ranging from natural disaster management to atmospheric pollution dispersion. Indeed, remotely sensed imagery is really an important source of data available to systematically and consistently characterize change in terrestrial ecosystems over time (Coops et al. 2006).2.    Methodology 2.1 Study areaThe Study area Jebel Marra is in western Sudan between Latitude 13° 45′ 00″ and Longitude 24° 30′ 00″The climate of the mountain is Mediterranean nature, where it rains almost the whole year round and that allows for the growth of abundant vegetation and clusters of dense forest trees.  Fig.1 shows the study area, Data Sources: Political Boundaries – CBS, UNMAS2.2 DataSatellite has been acquired from USGS the software used for this study includes Qigs, Arcgis, Erdas and ENVI 5 The data used for this study are described in Table 1. Images Used for the study Path/row Resolution Date of acquisition FormatLandsat 7 188/052 30 2001-11-12 GeotiffLandsat 8 188/052 30 2017-01-16 Geotiff2.3 Data preprocessing Data processed through the flowing means of 2.3.1 radiometric normalizationRadiometric normalization of the Images was achieved by applying flash setting calibrated type radiance using Envi 5.2.3.2 Atmospheric correctionAtmospheric correction of the Images was achieved by applying flash method using Envi 5.2.3.3 Pan sharpeningTo enhance spatial resolution for the two imageries, pan sharpening was applied using Erdas software 2.4 Image classificationPrior to change detection and image classification, Normalized Difference Vegetation Index (NDVI) image was generated for the two imageries then threshold classification technique was applied to the NDVI images for 2001 and 2003 using eCognition developer. change detection technique has been applied. Two data sets acquired at different dates are analyzed together to detect the locations where a change has occurred the change assessment.The NDVI images were all classified into five classes, forest_2001, forest decrease, forest increase, forest_2017, no change.                                   Fig. 5 shows the workflow of the classification4.   Results The Figures 2 and 3 below show the results of the classification, Figure 4 the change assessment. Change assessment, show that in 2001 the forest converges area was 485.9 km2, and in 2017 was 319.85 km2, it decreased by about 267km2 compared to 2001 Also it reveals that forest increased in some parts by about 101.33km2 in 2017 compared to 2001 and the unchanged area about 218.5km2.         Fig .2 shows the result of the classification of 2001 imagery                                                                  Fig. 3 shows the result of the classification of 2017 imagery Fig.4 shows the result of the change assessment4.   DiscussionThe result achieved the objective by showing that there significant decrease in forest cover. a possible explanation for the significant decrease in the forest cover related to direct effect of the armed conflict like cutting trees in frontlines, establishing camps, using artillery weapons. Also, might be related indirect effect for instance displacement people cutting forest for wood fuel, agricultural activities. To get more an accurate result from the classification different NDVI threshold has been applied. the result complies with the study carried out by UN mission in Sudan post-conflict assessment which shows that a very destructive pattern of land use change. The closed forest has been extensively degraded to burnt areas and open woodland, with a deforestation rate of 1.04 percent per annum. This clearing has not been matched by an increase in agricultural areas. The only gain has been a marginal increase in grazing land on the steep slopes. also, western Sudan has lost more that 30 percent of its forests since Sudan’s independence and rapid deforestation is ongoing. 5.   ConclusionsThe study demonstrates that there is a significant decrease in the forest coverage due to the direct and indirect effect of the armed conflict Also it reveals that there is a small increase of the forest in some part, however, half of the total area decreased compared to 2001.References Cavallaro, A. (2009). Change Detection for Object Segmentation. Smart Cameras,181-198. doi:10.1007/978-1-4419-0953-4_10  Gorsevski, V., Kasischke, E., Dempewolf, J., Loboda, T., & Grossmann, F. (2012). Analysis of the Impacts of armed conflict on the Eastern Afromontane forest region on the South Sudan — Uganda border using multitemporal Landsat imagery. Remote Sensing of Environment,118, 10-20. doi:10.1016/j.rse.2011.10.023Blaschke, T., Lang, S., & Hay, G. J. (2008). Object-based image analysis: spatial concepts for knowledge-driven remote sensing applications. Berlin: Springer.Gang Chen, Geoffrey J. Hay, Luis M. T. Carvalho & Michael A. Wulder (2012): Object-based change detection, International Journal of Remote Sensing, 33:14,4434-4457.(n.d.). Retrieved June 30, 2017, from http://drustage.unep.org/disastersandconflicts/sudan-post-conflict-environmental-assessmentYemshanov, D., Mckenney, D. W., & Pedlar, J. H. (2011). Mapping forest composition from the Canadian National Forest Inventory and land cover classification maps. Environmental Monitoring and Assessment,184(8), 4655-4669. doi:10.1007/s10661-011-2293-2Latifovic, R., & Olthof, I. (2003). Accuracy assessment of global land cover products derived from satellite data. doi:10.4095/220024Draulans, D., & Krunkelsven, E. V. (2002). The impact of war on forest areas in the Democratic Republic of Congo. Oryx,36(01). doi:10.1017/s0030605302000066Kabir Uddin (2014) forest cover mapping training manual Annex 1 Used and resulted geodata                 Raster features            Raster featuresRaster polygon features                                   Raster polygon features Annex 2 Used and resulted geodata                      Polygon features                                                         Polygon features