جستجوی مقالات مرتبط با کلیدواژه « Landsat-8 images » در نشریات گروه « جنگلداری »

Land cover change has become one of the most challenges due to the interaction of negative effects of climate change with human activities. In this study, land cover changes in the Hendijan Basin with a focus on the impact on dust sources located in Khuzestan province were investigated. Areas of forests, rangelands, woodlands, water, agricultural lands, residential and barren lands were extracted during 30 years at 1987 and 2017 using Landsat image processing. Dust sources located in Khuzestan have been monitored and land cover changes in have been revealed. The results show that trend of land cover changes inside and outside Khuzestan province is different from each other. In fact, the highest rate of change compared to beginning of study is occurred in mountainous part of the Hindijan Basin is due to increasing lakes of dams (9.7 times) and in the plain part due to the expansion of residential lands and also increasing cultivated area (8 and 4.9 times). Also, 936.31 square kilometers, has been added to barren lands, at the same time, the area of rangelands and forests has decreased 367.60 and 2633.40 square kilometers, respectively. Detection of changes in Khuzestan dust sources shows that the east and southeast area of Ahvaz, which was covered with vegetation and water (seasonal floods and flood plains) in winter of 1366, has become barren land in 1396. Monitoring land cover changes and reporting results helps managers make more informed decisions.

The forest type map is one of the most important thematic maps for forest ecosystem management. Forest mapping using field methods or aerial photos is labor-intensive and time consuming. In contrast, satellite data with its own characteristics like large and repetitive coverage, update and useful information in various wavelengths provides a good opportunity in this regard. This research was carried out with the aim of providing forest type map of central Zagros forests (Chahartagh forest reservoir), of Iran, using the Landsat 8 Operational Land Imager (OLI) data, in August 2016.Two ground-truth maps based on tree density and tree crown area were prepared by field surveying. Moreover, ancillary data such as tree species, location and crown area was taken. In order to increase the spatial resolution of multispectral bands, various image fusion techniques were applied. The best result obtained by the maximum likelihood algorithm with kappa coefficient and overall accuracy values of 57 and 63%, respectively. Due to high species diversity in this area the results showed that the OLI images have a moderate capability to produce forest type maps in Zagros forest.

Forests degradation and their convert to pasture, agriculture and urban or rural land use, occurs annually at high extent, in which make high damages to natural resources. In the current research, the logistic regression model was applied to identify effective factors of forest degradation and zoning possible forest degradation at Vaz and Lavij basins. Landsat Satellite images were used to identify land use variation within 1998 to 2006. Results showed that forest area was reduced from 81.97% in 1998 to 77.43% in 2006. Distance from village, river and road and also slope gradient were considered as independent variables, whereas land use change (forest degradation) was considered as dependent variable in the model. The logistic regression model was applied at two cases: classified and standardized variables. The obtained coefficient from the applied model at classified case indicated that forest degradation is more likely to happen at 0-100 m. distance from village, whereas the obtained coefficient from the applied model at the standardized case, indicated that there was negative relationship between the independent and dependent variables. Overall, zoning map of possible forest degradation at the studied area was developed at five levels: severe, high, moderate, low and very low, based on the calculated coefficients. According to the results achieved from modeling at classified case, 11.06 % of the studied area has potential for sever forest degradation.