Estimation of forest variables using remote sensing and GIS: Case study of Nabag Forest-South Kordofan, Sudan

Acacia senegal (gum Arabic tree) is one of the most important economic species in the Sudan and constitutes a large part of the forest stand in the country specially in Kordofan state. The gum Arabic tree in study area was facing a lot of endanger factors such as desertification, domination by invader natural species, illicit falling and week management plans. Therefore, there was a need for reliable estimate and update of information and condition regarding the tree management in sustainable bases. This study used remote sensing and GIS techniques together with field inventory to estimate the gum tree volume parameter. The study used ASTER L1b satellite imagery band 1, 2 and 3n for the year 2007 together with forest inventory. The procedure for forest variables estimate began with acquisition of the mentioned satellite imagery then registered, enhanced and subset. Afterwards, the subset was clustered into several strata using unsupervised classification ISODATA algorithm of ERDAS Imagine 9.1 software; and after with the help of the spectral profile tests and Principal Component Analysis (PCA), the image was clustered in to three classes. Afterward, the sample plots were selected randomly from the strata for collection of field data (diameter & height). With regards to remote sensing, the study used the Normalized Difference Vegetation Index (NDVI) to estimate the forest volume. The results showed that the volume produced from the NDVI was not significantly different from that calculated from the field data and what has been found in the literature. The study concluded that remotely-sensed data together with the field data were useful and effective in estimation of forest volume parameter in the study area and the NDVI was a good index for forest variable estimate.