Sudan as a Sahelian country faced numerous drought periods resulting in famine and mass immigration. Spatial data on dynamics of land use and land cover is scarce and/or almost nonexistent. The study area in the North Kordofan State is located in the centre of Sudan and falls in the Sahelian eco-climatic zone. The region generally yields reasonable harvests of rainfed crops and the grasslands supports plenty of livestock. But any attempts to develop medium- to longterm strategies of sustainable land management have been hampered by the impacts of drought and desertification over a long period of time. This study aims to determine and analyse the dynamics of change of land use/land cover classes. The study attempts also to improve classification accuracy by using different data transformation methods like PCA, TCA and CA. In addition it tries to investigate the most reliable methods of pre-classification and/or post-classification change detection. The research also attempts to assess the desertification process using vegetation cover as an indicator. Preliminary mapping of major soil types is also an objective of this study. Landsat data of MSS 187/51 acquired on 01.01.1973 and ETM+ 174/51 acquired on 16.01.2001 were used. Visual interpretation in addition to digital image processing was applied to process the imagery for determining land use/land cover classes for the recent and reference image. Pre- and post-classification change detection methods were used to detect changes in land use/land cover classes in the study area. Pre-classification methods include image differencing, PC and Change Vector Analysis. Georeferenced soil samples were analysed to measure physical and chemical parameters. The measured values of these soil properties were integrated with the results of land use/ land cover classification. The major LULC classes present in the study area are forest, farm on sand, farm on clay, fallow on sand, fallow on clay, woodyland, mixed woodland, grassland, burnt/wetland and natural water bodies. Farming on sandy and clay soils constitute the major land use in the area, while mixed woodland constitutes the major land cover. Classification accuracy is improved by adopting data transformation by PCA, TCA and CA. Pre-classification change detection methods show indistinct and sketchy patterns of change but post-classification method shows obvious and detailed results. Vegetation cover changes were illustrated by use of NDVI. In addition preliminary soil mapping by using mineral indices was done based on ETM+ imagery. Distinct patterns of clay, gardud and sand areas could be classified. Remote sensing methods used in this study prove a high potential to classify land use/land cover as well as soil classes. Moreover the remote sensing methods used confirm efficiency for detecting changes in LULC classes and vegetation cover during the addressed period.

The present study was conducted in Umfakarin natural forest reserve, South Kordofan, Sudan. The main objective was to investigate the possibility of managing Acacia seyal Del. variety seyal for the production of gum talha. Three stand densities (strata), namely dense, medium, and slight, were distinguished based on the number of trees per hectare. During the sampling phase, the study adopted the method of identifying the competitors (neighboring trees) from the subject one (trees selected for gum production experiments). From the three stand densities, a total of 482 subject trees, covering variable diameter ranges (d= 9-11.5, 13.5-16, 18-20.5 and above 21 cm) were selected, based on the diameter at 0.25 m height (d0.25). In each stratum, competitor trees were identified within a radius equal to the height of subject tree multiplied by a factor (1.25). The diameter at breast height, height to crown base, height, crown radii, and tree coordinates were measured for each of the subject trees and its competitors. Subject trees were exposed to tapping on first of October, the fifteenth of October, and the first of November, using local tools (Sonki and Makmak). Additionally, untapped trees were used as controlling-variables. The initial gum collection was completed fifteen days after the tapping, while the subsequent (7-9 pickings) were done at an interval of fifteen days. Six stand height functions were tested and the results illustrated that the Michailow stand height function was suitable for predicting the height of Acacia seyal in Umfakarin natural forest. The predictive ability of this height function ranged from 19.3% to 24%. The volume function used in this study was able to predict the volume of standing trees with more than 92 percent accuracy. Competition among trees of Acacia seyal was assessed in terms of competition indices. Eight competition indices were quantified using the CroCom program. The relationship between these indices and tree dimensions (diameter at breast height, height and crown diameter) was tested using logarithmic models. Among these indices, the Hegyi_2 index is considered a suitable index to be applied for estimating the degree of competition in natural stands of A. seyal of dense stratum when using diameter at breast height as a predictor. About 70% of the total variability is explained by this logarithmic model. Gum yielded by each subject tree per season was obtained by summing up the gum samples collected from all pickings. Gum production per unit area was also determined. Regression tree, general linear model (GLM) and logistic regression techniques were used for analyzing the obtained data. The results of the study indicated that the gum yield is independent of stand density. Tapping has influence on gum yield. Trees tapped by sonki on the first of October at medium stand density have the highest gum with an average value of about 56 g/tree/season. Significant difference (p = 0.021) was detected between two groups of dates; the first of October and first of November in medium stand density. The results also revealed that the most important variable influencing gum production was found to be diameter at breast height with 23.95 cm threshold. Between 41-53 percent of subject trees produce gum less than 50 g/season. The results indicated that A. seyal species produces a very low quantity of gum talha (3.6-4.8 kg/ha) and for economic reasons, its tapping is not recommended. The findings of the regression analysis revealed to a model which could be used to estimate the yield of gum talha from A. seyal natural stands in the Umfakarin forest, South Kordofan, Sudan. Conducting experiments on the production of gum talha in permanent plot trials in different climatic regions of the Sudan is highly recommended.

The gum arabic belt in Sudan plays a significant role in environmental, social and economical aspects. The belt has suffered from deforestation and degradation due to natural hazards and human activities. This research was conducted in North Kordofan State, which is affected by modifications in conditions and composition of vegetation cover trends in the gum arabic belt as in the rest of the Sahelian Sudan zone. The application of remote sensing, geographical information system and satellites imageries with multi-temporal and spatial analysis of land use land cover provides the land managers with current and improved data for the purposes of effective management of natural resources in the gum arabic belt. This research investigated the possibility of identification, monitoring and mapping of the land use land cover changes and dynamics in the gum arabic belt during the last 35 years. Also a newly approach of object-based classification was applied for image classification. Additionally, the study elaborated the integration of conventional forest inventory with satellite imagery for Acacia senegal stands. The study used imageries from different satellites (Landsat and ASTER) and multi-temporal dates (MSS 1972, TM 1985, ETM+ 1999 and ASTER 2007) acquired in dry season (November). The imageries were geo-referenced and radiometrically corrected by using ENVI-FLAASH software. Image classification (pixel-based and object-based), post-classification change detection, 2x2 and 3x3 pixel windows and accuracy assessment were applied. A total of 47 field samples were inventoried for Acacia senegal tree’s variables in Elhemmaria forest. Three areas were selected and distributed along the gum arabic belt. Regression method analysis was applied to study the relationship between forest attributes and the ASTER imagery. Application of multi-temporal remote sensing data in gum arabic belt demonstrated successfully the identification and mapping of land use land cover into five main classes. Also NDVI categorisation provided a consistent method for land use land cover stratification and mapping. Forest dominated by Acacia senegal class was separated covering an area of 21% and 24% in the year 2007 for areas A and B, respectively. The land use land cover structure in the gum arabic belt has obvious changes and reciprocal conversions between the classes indicating the trends and conditions caused by the human interventions as well as ecological impacts on Acacia senegal trees. The study revealed a drastic loss of Acacia senegal cover by 25% during the period of 1972 to 2007.The results of the study revealed to a significant correlation (p ≤ 0.05) between the ASTER bands (VNIR) and vegetation indices (NDVI, SAVI, RVI) with stand density, volume, crown area and basal area of Acacia senegal trees. The derived 2x2 and 3x3 pixel windows methods successfully extracted the spectral reflectance of Acacia senegal trees from ASTER imagery. Four equations were developed and could be widely used and applied for monitoring the stand density, volume, basal area and crown area of Acacia senegal trees in the gum arabic belt considering the similarity between the selected areas. The pixel-based approach performed slightly better than the object-based approach in land use land cover classification in the gum arabic belt. The study come out with some valuable recommendations and comments which could contribute positively in using remotely sensed imagery and GIS techniques to explore management tools of Acacia senegal stands in order to maintain the tree component in the farming and the land use systems in the gum arabic belt.

The present study was conducted in Umfakarin natural forest reserve, South Kordofan, Sudan. The main objective was to investigate the possibility of managing Acacia seyal Del. variety seyal for the production of gum talha. Three stand densities (strata), namely dense, medium, and slight, were distinguished based on the number of trees per hectare. During the sampling phase, the study adopted the method of identifying the competitors (neighboring trees) from the subject one (trees selected for gum production experiments). From the three stand densities, a total of 482 subject trees, covering variable diameter ranges (d= 9-11.5, 13.5-16, 18-20.5 and above 21 cm) were selected, based on the diameter at 0.25 m height (d0.25). In each stratum, competitor trees were identified within a radius equal to the height of subject tree multiplied by a factor (1.25). The diameter at breast height, height to crown base, height, crown radii, and tree coordinates were measured for each of the subject trees and its competitors. Subject trees were exposed to tapping on first of October, the fifteenth of October, and the first of November, using local tools (Sonki and Makmak). Additionally, untapped trees were used as controlling-variables. The initial gum collection was completed fifteen days after the tapping, while the subsequent (7-9 pickings) were done at an interval of fifteen days. Six stand height functions were tested and the results illustrated that the Michailow stand height function was suitable for predicting the height of Acacia seyal in Umfakarin natural forest. The predictive ability of this height function ranged from 19.3% to 24%. The volume function used in this study was able to predict the volume of standing trees with more than 92 percent accuracy. Competition among trees of Acacia seyal was assessed in terms of competition indices. Eight competition indices were quantified using the CroCom program. The relationship between these indices and tree dimensions (diameter at breast height, height and crown diameter) was tested using logarithmic models. Among these indices, the Hegyi_2 index is considered a suitable index to be applied for estimating the degree of competition in natural stands of A. seyal of dense stratum when using diameter at breast height as a predictor. About 70% of the total variability is explained by this logarithmic model. Gum yielded by each subject tree per season was obtained by summing up the gum samples collected from all pickings. Gum production per unit area was also determined. Regression tree, general linear model (GLM) and logistic regression techniques were used for analyzing the obtained data. The results of the study indicated that the gum yield is independent of stand density. Tapping has influence on gum yield. Trees tapped by sonki on the first of October at medium stand density have the highest gum with an average value of about 56 g/tree/season. Significant difference (p = 0.021) was detected between two groups of dates; the first of October and first of November in medium stand density. The results also revealed that the most important variable influencing gum production was found to be diameter at breast height with 23.95 cm threshold. Between 41-53 percent of subject trees produce gum less than 50 g/season. The results indicated that A. seyal species produces a very low quantity of gum talha (3.6-4.8 kg/ha) and for economic reasons, its tapping is not recommended. The findings of the regression analysis revealed to a model which could be used to estimate the yield of gum talha from A. seyal natural stands in the Umfakarin forest, South Kordofan, Sudan. Conducting experiments on the production of gum talha in permanent plot trials in different climatic regions of the Sudan is highly recommended.

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Multi-temporal remotely sensed data (MSS, TM and ETM+)were used for monitoring and mapping the desertification processes in North Kordofan State, Sudan.A liear mixture model (LMM) was adopted to analyse and the desertification proccesses by using the image endmembers. interpretation of ancillary data and field observation was adopted to verfiy the role of human impacts in the temporal changes in the study area. The findings of the study proved the powerfull of remotely sensed data in monitoring and mapping the desertification processes and come out with valuable recommendations which could contribute positively in reducing desert encroachment in the area.

Multi-temporal remotely sensed data (MSS, TM and ETM+)were used for monitoring and mapping the desertification processes in North Kordofan State, Sudan.A liear mixture model (LMM) was adopted to analyse and the desertification proccesses by using the image endmembers. interpretation of ancillary data and field observation was adopted to verfiy the role of human impacts in the temporal changes in the study area. The findings of the study proved the powerfull of remotely sensed data in monitoring and mapping the desertification processes and come out with valuable recommendations which could contribute positively in reducing desert encroachment in the area.