Journal of RS and GIS for Natural Resources
Year:2020 | Volume:11 | Issue:1 (38)|Papers Count:6

Remote sensing techniques have been applied to estimate the spatiotemporal distribution of evapotranspiration as an alternative for field measuring methods. The objective of this study is to estimate the maximum daily demand of rain-fed wheat from green water resources in Ahar county using the SEBAL algorithm and MODIS images. First, the results from the SEBAL algorithm were evaluated by comparing them to the results from the Eagleman-Affholder method in rainfed wheat fields and to the results from Fao-Penman-Monteith method in irrigated wheat fields and then the wheat maximum daily evapotranspiration values were calculated in the study area. MODIS surface reflectance and land surface temperature images were used to monitor the variation of normalized difference vegetation index during the wheat growth period, to map wheat areas and to estimate wheat evapotranspiration during the wheat booting stage until the wheat yellowing stage in 2010. After evaluating the SEBAL algorithm during the mentioned period, the maximum daily demand of rain-fed wheat from green water resources was estimated on the 17th of July 2019. By comparing the wheat evapotranspiration values from SEBAL and from computational methods, the average absolute error and correlation were calculated as 0. 61 mm/day and 0. 9 respectively. It was also found that the wheat highest evapotranspiration occurred after the vegetation index curve had reached its peak. In this time, the wheat maximum daily water demand from the green water resources throughout the county in 2019 was estimated almost equal to 0. 93 million cubic meters.

Determining and identifying homogeneous regions for ecosystem services supply is an effective and useful step in improving land management. Therefore, in this study, after quantifying and mapping ecosystem services, aesthetic value, recreational value, and noise pollution reduction, the K-Means clustering method was used to identify homogeneous areas of ecosystem service supply and homogeneous areas zoning was prepared in the GIS environment. To investigate the effective parameters on ecosystem services supply, the slope, altitude, population density, distance from access routes, distance from the river, percentage of available land uses and distance from the centre of the largest urban region were extracted for each homogeneous area or cluster. Based on the Davis-Bouldin validation index, the optimal number of clusters was 4. Cluster number two with the area of 686. 27 Km2 was the largest, while cluster number one with the area of 119. 75 Km2 was the smallest in the area. Investigation of environmental-social parameters showed that land use has the highest impact on ecosystem services supply. The results showed that there is a direct relationship between these parameters and ecosystem services supply in each cluster. Based on the results of this study, investigation of homogeneous areas of ecosystem services can be effective to improve land use planning and management.

Regarding stop industrial forests exploitation in northern Iran, this study was conducted to determine the suitable areas for wood farming by Eucalyptus in Khuzestan province using Fuzzy AHP and GIS. The indices used were included four main indices, water resources, land use, climate and soil and 22 related sub-indices (Soil texture, soil depth, soil salinity, soil acidity, distance from the river, water volume, surface water salinity, groundwater depth, groundwater salinity, mean annual temperature, mean minimum annual temperature, mean maximum annual temperature, minimum absolute temperature, maximum absolute temperature, mean annual rainfall, mean annual relative humidity, mean annual wind speed, shrublands around the rivers, sandy hills, Jihad Nasr lands, empty lands around the Jihad Nasr channels, empty lands around the water resources). Maps of these factors were prepared using Google Earth satellite imagery (from summer of 2017 to 2019), ground sampling (September of 2019) and available data. Eucalyptus cultivation map in Khuzestan province was also prepared from Khuzestan Natural Resources Administration and ground sampling by GPS in September of 2019. The land use map was prepared by the digitization of land use/cover using Google Earth satellite imagery from summer of 2017 to 2019. Accuracy of land use map was evaluated by 60 ground control points. The weight of effective indices in Eucalyptus wood farming potential was calculated using Fuzzy AHP. For this purpose, 30 expert questionnaires (30 expert judgments) were distributed among the scientific and operating experts of wood farming to express the importance and priority of effective factors in wood farming. Then, the mean questionnaire was obtained and it was analysed by Chang triangular fuzzy extent analysis. Based on this method, the normal weights of the indices and subindices were calculated using Fuzzy AHP method. Using the linear weighted combination of effective sub-indices, maps of the main indices and then a map of Eucalyptus wood farming potential was prepared. Finally, the wood farming potential map was validated by Eucalyptus cultivation map and its accuracy was evaluated in identifying the suitable areas for Eucalyptus wood farming in Khuzestan province. The results showed that among the main indices, water resources and land use had the most importance in the determination of the prone lands for Eucalyptus farming in Khuzestan province based on the Fuzzy AHP. According to the results, 12. 83% of the area had very good potential and 10. 47% of the area had good potential for Eucalyptus farming. The results of the accuracy assessment of wood farming potential map also showed that Fuzzy AHP with overall accuracy 82% had good accuracy in identification of the prone areas for wood farming in Khuzestan province.

The estuaries are one of the most important coastal natural resources. Chlorophyll-a (Chl-a) monitoring, which is the pigment of oceanic and coastal phytoplankton, can be measured and evaluated using new remote sensing technology. The presence of blue, green and red wavelengths in oceanic observation satellites has always paved the way for monitoring the color of the oceans. The aim of this study is used OC2 and OC3 bio-optical algorithms and Sentinel-2 MSI and Landsat-8 OLI satellite data in April 2019 to estimate chlorophyll-a concentration in the estuary Tiap area. Ground sampling data were carried out to correlate and evaluate the results. The results showed that the OC2 algorithm in Landsat-8 and Sentinel-2 satellites had the highest R Squared coefficient (R2) 0. 91 and 0. 64, respectively, and the Root mean square error (RMSe) of the satellite images were 0. 13 and 0. 33, respectively. These results indicate the high accuracy of the OC2 algorithm in the satellite images used and were selected as the most suitable algorithm for mapping chlorophyll-a concentration in the study area.

Remote sensing technique is one of the most effective tools for monitoring, studying and determining the cultivation area of agricultural and horticultural crops, especially on a large scale. Planners, managers, and farmers, with knowledge of the type and extent of crop cultivation, can adopt appropriate management and enforcement policies. The purpose of the present study was to evaluate the supervised classification ability to classify Landsat 8 and Sentinel-2A multi-band satellite imagery in determining the cultivated area and type of four varieties of Pistachio namely such as; Akbari, Kalle Ghuchi, Ahmad Aghaei and Fandooki in an orchard in the Yazd province. In the present study, the accuracy of four classification algorithms, namely: Parallelepiped classification, Minimum distance, Mahalanobis distance and Maximum likelihood, as well as the optimum time in the separation of pistachio cultivars, were investigated. According to the classification results of a Landsat-8 image, on June 12, 2018, the Maximum likelihood algorithm with a final accuracy and Kappa coefficient of 76. 8% and 0. 67% and Parallelepiped classification algorithm with the final and Kappa coefficients of 64. 7 and 0. 47, were of highest and lowest accuracy among others, respectively. Also, according to the results, the best time for the separation of Pistachio cultivars was in late June. The Kappa coefficient of maximum likelihood classification algorithm on June 22, July 23, August 24 and September 25 of 2018 were 0. 67, 0. 64, 0. 63 and 0. 63, respectively. The final accuracy and Kappa coefficient of maximum likelihood classification algorithm on the Sentinel-2A Satellite images on 12 June 2018, were 80% and 0. 71, respectively. By applying the median filter with a 3×3 dimensional kernel window size on the classified image, the final accuracy and Kappa coefficient was increased to 82. 6% and 0. 75, respectively. The final accuracy and Kappa coefficient of classification and separation of Pistachio cultivars in Sentinel-2A images were higher than in Landsat-8 images. Overall, based on our results, the remote sensing classification techniques, as well as multi-spectral satellite imagery, are suitable for agricultural and horticultural mapping.

Zagros arid woodlands are among the most important and valuable areas of Iran΄ s natural resources that due to the dieback of trees in recent years, it seems necessary to manage and rehabilitate this vegetation. This research was aimed to study the spatial distribution map dieback of Persian oak trees (Quercus brantii Lindl), analyze and describe the spatial distribution using a combination of geostatistical techniques and remote sensing in Barm plain, Fars province. First, the RapidEye satellite image was classified into two categories of healthy and dried trees with the supervised classified algorithm including maximum likelihood. The overall accuracy and Kappa coefficients were 80% and 73%, respectively. The data were then collected in circular sample plots of 2000 m2 (with a radius of 25. 24 m) based on a 300×300 meter network in a randomized manner. After preparing the point map, the percentage of drying of the classified image was determined by simple, ordinary, and universal Kriging interpolation method, which were evaluated using three models: Exponential, spherical, Gaussian methods. After evaluating the data using the cross-evaluation results, the most accurate fitting was shown by the simple Kriging method with the exponential model (mean estimation error of 0. 023). Dieback map was obtained with classes of zero to 10, 10-20, 20-30, 30-40 and more than 40%. The largest area was related to class 20% to 30% with 493. 9 ha and the smallest area was for zero to 10%, with 70. 46 ha. The present study showed that it is possible to obtain maps of the spatial distribution of Persian oak dieback and recognize the focal points using geostatistical techniques and remote sensing.