maximum likelihood algorithm

Investigating spatial and temporal changes in the composition of plant species and communities is an essential step in assessing pasture health conditions, understanding the evolutionary processes of the local ecosystem, and developing rangeland management strategies. The aim of this study is to evaluate the ability of supervised classification of Landsat 8 satellite images in mapping the plant communities in the southern rangelands of Yazd province. To do so, we selected and sampled 90 training samples from areas that showed a homogeneous composition of plant species up to a minimum radius of 60 meters from the central point in 2015, and then; plant communities were separated based on the prevalence of the percentage of cover crown. A Landsat 8 satellite image of OLI sensor on May 29, 2015 was used after geometric, atmospheric and radiometric corrections. In the present study, the final accuracy of six supervised classification algorithms including parallel classification algorithms, the minimum distance, the Mahalanobis distance, the maximum likelihood, the neural network and support vector machine with radial kernel were examined in the separation and determination of the range of the plant community. Based on the results, the maximum likelihood algorithm and the neural network had the highest accuracy with the final accuracy of 96.4% and 84.8% and Kappa coefficient of 0.95 and 0.82, respectively. In general, the results of this study showed that in order to differentiate and classify different plant communities in the study area, the maximum likelihood algorithm has good results and combining field studies with remote sensing is a very suitable capability in segregating and classifying the plant types and communities.

Land use change are the result of complex interactions between many processes. Many models have been developed in recent decades to understand these dynamics. The present study, using Landsat satellite images and applying the maximum probability classification algorithm, examined the land use pattern of Simine River basin in 2000, 2005, 2010 and 2017 and identifies the historical transformations of them. Finally, the various scenarios of land use change in 2040 will be examined using the latest version of the Dyna-CLUE land use model. The results of modeling indicate Dyna-CLUE model performance in analyzing and predicting future land use patterns at the basin level and its calibration and validation coefficients in 2005 and 2010 are 0.84 and 0.69, respectively. Analysis of land use changes between 2000 and 2017 has shown that the most change was the conversion of rangeland lands to irrigated lands; So that about 343 square kilometers of rangeland lands (equivalent to 13%) decreased and irrigated agricultural lands increased by about 127%. Also, the spatial arrangement of land use classes in 2040 in the form of three optimistic, pessimistic and the continuation of the previous trend scenarios and its outputs have shown that the future pattern of most land use classes in the region has not changed much compared to historical spatial patterns and shows a marginal spatial expansion. The results of this study can be used as an effective tool for environmental planners to manage the future infrastructure of the Simine River basin.