N. Zahedifard, S. J. Khajeddin, A. Jalalian,
Volume 8, Issue 2 (7-2004)
Abstract
Satellite data use is finding global applications because they provide repeated cover, broad information, high electromagnetic spectral resolution, and software-hardware compatibilities. This study aims to evaluate of the Landsat TM data capabilities in land-use mapping of Bazoft River basin (Chahar Mahale Bakhtiary Province). Six spectral bands of the Landsate TM were employed to produce land-use map of the Region. The date of image acquisition was May 5th, 1998. Performance of the geometric correction completed with RMSE= 1.008 pixels. Various image enhacement methods (e.g. FCC, filtering and Vegetation Indices) were used to study the different land-covers. Field investigations were carried out using a GPS, 1:50000 scale topographic map and false color composites images. Heterogeneous land-use units were studied in 62 sample sites estimating percentage of vegetation cover. A regression analysis was performed between percentage vegetation covers and vegetation indices values of NDVI, RVI, SAVI, DVI, TSAVI1, NRVI and MSAVI2. Results show that NDVI, SAVI, TSAVI1, NRVI and MSAVI2 have high correlation coefficients. But RVI, DVI and PVI have low correlation coefficients. The resulting values of vegetation cover were density sliced to produce the land-cover map. After supervised classifications and density slicing of Vegetation Indices, classifacation accuracy was assessed and, finally, land-use map of the study area was produced with Hybrid classification method. Supervised classification with maximum likelihood method was the best technique for land-use mapping in the study area the total Kappa index was %87. In general, detection of some land-use classes through single date TM data is not feasible, these include: scattered forest trees with cultivated understory, annual grasses, and fallow lands. Also TM digital data are incapable of distinguishing small and separated rural constructions or soil-covered routes.
A.r. Emadi, S. Fazeli, M. Hooshmand, S. Zamanzad-Ghavidel, R. Sobhani,
Volume 27, Issue 1 (5-2023)
Abstract
The agricultural sector as one of the most important sectors of water consumption has great importance for the sustainability of the country's water resources systems. The objective of this study was to estimate the river water abstraction (RWA) for agricultural consumption in the study area of Nobaran in the Namak Lake basin. The RWA was estimated using variables related to morphological, hydrological, and land use factors, as well as a combination of their variables collected through field sampling. Data mining methods such as adaptive-network-based fuzzy inference systems (ANFIS), group method of data handling (GMDH), radial basis function (RBF), and regression trees (Rtree) were also used to estimate the RWA variables. In the current study, the GMDH24 model with a combined scenario including the variables of river width, river depth, minimum flow, maximum flow, average flow, crop, and the garden cultivated area was adopted as the best model to estimate the RWA variable. The RMSE value for the combined scenario of the GMDH24 model was found to be 0.046 for estimating RWA in the Nobaran study area. The results showed that the performance of the GMDH24 model for estimating RWA for maximum values is very acceptable and promising. Therefore, modeling and identifying various variables that affect the optimal RWA rate for agricultural purposes fulfills the objectives of integrated water resources management (IWRM).
I. Kazemi Roshkhari, A. Asadi Vaighan, M. Azari,
Volume 28, Issue 1 (5-2024)
Abstract
Due to climate change and human activities, the quality and quantity of water have become the most important concern of most of the countries in the world. In addition, changes in land use and climate are known as two important and influential factors in discharge. In this research, four climate change models including
HADGEM2-ES, GISS-E-R, CSIRO-M-K-3-6-0, and CNRM-CM5.0 under two extreme scenarios RCP2.6 and RCP8.5 were used as climate change scenarios in the future period of 2020-2050. The future land use scenario (2050) was prepared using the CA-Markov algorithm in IDRISI software using land use maps in 1983 and 2020. The SWAT model was calibrated to better simulate hydrological processes from 1984 to 2012 and validated from 2013 to 2019 and was used to evaluate the separate and combined effects of climate change and land use on discharge. The prediction of the climate change impact on discharge showed a decrease in most of the models under the two scenarios RCP2.6 and RCP8.5. The average maximum decrease and increase under the RCP2.6 scenario is 60 and 30 percent, respectively. This significant reduction is greater than that predicted under the RCP8.5 scenario. Examining the combined effects of climate and land use change revealed that the average decrease in discharge in the months of October, November, December, and January under two scenarios is 46.2 and 58%, respectively. The average increase in discharge is predicted to be 47% under the RCP8.5 in the months of April and May in the HadGEM2ES.
