استخدام تقنات التحسس النائي لإدارة و تقييم الغابة الحضرية في نينوى

Abstract EN

This study was conducted in a forest of Nineveh, located in the left side of the city of Mosul, at the intersection of latitude 36° 20' 24" north and longitude 43° 7' 48" to the east.

The total studied area was 2509 acres in order to assess the management urban forest of the Nineveh depending on Supervised Classification for the satellite image using Erdas imagine 9.1 program for the Nineveh Forest and around of the USA satellite Land sat and sensor thematic mapper improved ETM + with a spatial accuracy 14,25 meters.

The results out of classification were all of the buildings, roads, trees, conifers trees, broad-leaved, vegetation, water, empty terrains, building looked very clear and appeared white and river colored in blue as well as we were able to see wooded areas, which appeared green, and empty terrains in brown and covered with grass and herbal which appeared in cyan and the paved roads marked in red.

The results of the satellite image showed that the highest percentage of cover in Nineveh and around was the forest and reached 28.38% while the river rate was 16.68%, followed by the class of the tourist use which was 11.90%, and then followed by roads their approaches amounted to be 10.88% empty terrains rate was 8.74% and 7.01% for islands, followed by the farmlands to be 5.69% and residential land was 4.47%, followed by buildings to be 0.71% and finally Al-kharraze stream of the studied area accounted for 0.40%.

Noting the result, it was found that the tourist class was the most encroachment on forest area and tourism is still going on encroachment on the original forest area, followed by the roads, and their approaches that took up of the forest area.

As for the forest trees of Nineveh forest, the results indicated that Eucalyptus was the largest forest area and was the highest in the coverage ratio, which amounted to be 70.97%, while the Casuarina was the lowest among the trees of the forest area and was the least in the coverage ratio, which amounted to be 0.36%.

The results showed that the overall accuracy of the classification satellite imagery amounted to be 6.94%, while the overall error of classification accounted for 5.4% only and this means that it was accurate to rely on the satellite imagery in classification.