جستجوی مقالات مرتبط با کلیدواژه "landuse changes" در نشریات گروه "منابع طبیعی"

The aim of this stydy is to proced the trend process of land cover changes as a result of land use changes into dry land using satellite images, remote sensing and GIS softwares and to find out the type and magnitude of changes by the image sensor Landsat ETM in 2000 and sensor of Landsat OLI in 2014 using ENVI®5 software and unsupervised classification methods in Dehsard and kohsefeid, Kerman province. Finally, land use classification map including rangelands, agricultural lands and gardens was specified. In order to produce the training points, field visits, GPS and Google Earth software were utilized. Supervised classification was used to assess the accuracy of classification images. Afterwards, KAPA coefficie was applied to calculate the precision of produced maps. Precision matrix was created for each map. For the detection and assessment of land use changes as compared to the others, Crosstab was used. The results of land use changes in two regions showed that in this regard, the rangelands had the most changes estimated as 77% and 73% for Dehsard and Kohsefid watersheds, respectively. The precision of classification maps was given as 98% for both watersheds. The results of this study showed that the expansion of agricultural activities concerning the rangeland ecosystems could change the rangelands into the lands with low efficiency and in two mentioned regions, 9% and 20% of changes occurred in the low efficiency lands in Dehsard nd Kohsefid watersheds, respectively.

Monitoring and optimal management of natural resources is requiring an update and accurate information. In this context, land use/cover maps is considered as a one of the most important sources of information on natural resources management. Optimal management of resources requires assessment and understanding of the changes and degradation of resources in the past. It also needs to have an accurate plan in order to control and inhibition of the happened destruction potential in future. The Markov chain model is one of the most efficient methods for predicting changes in land use and land cover. In this research, land cover changes in previous years and the possibility of predicting in the future are investigated in Bostagh plain using the Markov chain model. Therefore, using MSS (1987), ETM+ (2002) and OLI (2014) images sensors and region ancillary information, land use map is provided and 2024 land use map is predicted too. Land use maps were performed using kappa coefficient after correcting satellite images, determining training samples, and evaluating classification accuracy. According to the results, bare/barren and rangeland classes are the most dynamic existing usage in the region. The area percentage of these lands during 1987 to 2014 was 21.64% subtractive and 31.14% additive respectively. This represents a total degradation and replacement of the weaker use in the region. The results of predicting changes in the time interval 2014-2024, showed that 98% of residential lands, 88% of bare land, 77% of saline land, 45% of rangeland, and 37% of agriculture will remain unchanged. Moreover, the conversion of rangeland to bare land (41.94%) are the highest, and the conversion of bare lands to residential lands (0.02%) and rangeland to residential lands (0.03%) are the lowest possibility of conversion. Predicting maps derived from the Markov chain model are very important to provide an overview for better natural resources management. Landuse changes Satellite images Predict of changes Markov chain model Bostagh plain