Accuracy of pixel-based classification: application of different algorithms to landscapes of Western Iran.

Scenarios for monitoring land cover on a large scale, involving large volumes of data, are becoming more prevalent in remote sensing applications. The accuracy of algorithms is important for environmental monitoring and assessments. Because they performed equally well throughout the various research regions and required little human involvement during the categorization process, they appear to be resilient and accurate for automated, big area change monitoring. Malekshahi City is one of the important and at the same time critical areas in terms of land use change and forest area reduction in Ilam Province. Therefore, this study aimed to compare the accuracy of nine different methods for identifying land use types in Malekshahi City located in Western Iran. Results revealed that the artificial neural network (ANN) algorithm with back-propagation algorithms could reach the highest accuracy and efficiency among the other methods with kappa coefficient and overall accuracy of approximately 0.94 and 96.5, respectively. Then, with an overall accuracy of about 91.35 and 90.0, respectively, the methods of Mahalanobis distance (MD) and minimum distance to mean (MDM) were introduced as the next priority to categorize land use. Further investigation of the classified land use showed that good results can be provided about the area of the land use classes of the region by applying the ANN algorithm due to high accuracy. According to those results, it can be concluded that this method is the best algorithm to extract land use maps in Malekshahi City because of high accuracy.

The monetary facilities payment for ecosystem services as an approach to restore the Degraded Urmia Lake in Iran.

This study analyzed the potential use of Payment for Ecosystem Services (PES) as a strategy for improving water supply management. This study focused on the Siminehroud Sub-basin due to its high importance to the Basin of Urmia Lake (UL). Siminehroud is the second provider of water (by volume) to Urmia Lake. To evaluate the technical and economic feasibility of a PES scheme, the current land use map was extracted using satellite imagery. In addition, the two algorithms of Support Vector Machines (SVMs) and Maximum Likelihood (ML) are used for Landsat images classification, rather than analyzing the relationship between land use and ecosystem services. Then, the most relevant ecosystem services provided in the region were evaluated using the Benefit Transfer Method. In the last step, by designing and implementing a survey, on the one hand, the local farmers' Willingness to Accept (WTA) cash payments for reducing the area they cultivate, and on the other hand, the farmers' Willingness to Pay (WTP) for managing the water consumption were determined. The results illustrated that the WTA program is more acceptable among the beneficiaries. It is also notable that this program needs very high governmental funding. Furthermore, the results of the program indicate that the land area out of the cultivation cycle will gradually increase while the price of agricultural water will also increase.

Urban morphology detection and it's linking with land surface temperature: A case study for Tehran Metropolis, Iran.

Expansion of urban areas and alteration of natural land cover exacerbate the local climate change. To find out the effect of land cover changes on the local climate, in this study, the Local Climate Zone (LCZ) concept was utilized to detect urban morphology in Tehran Metropolis. LCZ and Land Surface Temperature (LST) can be identified and classified based on available remote sensing products. Firstly, LCZ maps of Tehran metropolis were extracted using Landsat imagery, and secondly, relationships between LCZ and LST were explored for three years (1990, 2004, and 2018). We found that Tehran urban structure has 13 LCZs based on imagery from Landsat 5 and 14 LCZs based on images for Landsat 7 and 8. Overall accuracy and kappa coefficient were estimated at 62% and 0.60, respectively. Results show that built-up classes including compact high-rise, compact mid-rise, and heavy industrial areas tended to increase the surface temperature, while except for bare land, all other land cover types tended to decrease the surface temperature. The findings also suggest that complementary optical and thermal remote sensing data, such as the combination of OLI with TIRS imageries, were sufficient for supervised LCZ and LST classification in a semi-arid region of Tehran metropolis.

Transboundary Basins Need More Attention: Anthropogenic Impacts on Land Cover Changes in Aras River Basin, Monitoring and Prediction.

Changes in land cover (LC) can alter the basin hydrology by affecting the evaporation, infiltration, and surface and subsurface flow processes, and ultimately affect river water quantity and quality. This study aimed to monitor and predict the LC composition of a major, transboundary basin contributing to the Caspian Sea, the Aras River Basin (ARB). To this end, four LC maps of ARB corresponding to the years 1984, 2000, 2010, and 2017 were generated using Landsat satellite imagery from Armenia and the Nakhchivan Autonomous Republic. The LC gains and losses, net changes, exchanges, and the spatial trend of changes over 33 years (1984-2017) were investigated. The most important drivers of these changes and the most accurate LC transformation scenarios were identified, and a land change modeler (LCM) was applied to predict the LC change for the years 2027 and 2037. Validation results showed that LCM, with a Kappa index higher than 81%, is appropriate for predicting LC changes in the study area. The LC changes observed in the past indicate significant anthropogenic impacts on the basin, mainly by constructing new reservoir dams and expanding agriculture and urban areas, which are the major water-consuming sectors. Results show that over the past 33 years, agricultural areas have grown by more than 57% from 1984 to 2017 in the study area. Results also indicate that the given similar anthropogenic activities will keep on continuing in the ARB, and agricultural areas will increase by 2% from 2017 to 2027, and by another 1% from 2027 to 2037. Results of this study can support transboundary decision-making processes to analyze potential adverse impacts following past policies with neighboring countries that share the same water resources.