In case the performance of irrigation and drainage systems could be monitored by using satellite data, which are taken in short intervals, the problems concerning these systems could be corrected. Roodasht region which is located in the lower part of the Zayanderood River Basin was considered as a pilot plain. The basin is struggling with salinity and waterlogging which started by construction of the Zayanderood Dam and consequently, doubling the share of irrigation water of the area. For this purpose the satellite images of LANDSAT MSS and TM of 1976 through 1990 were used. Modifications was performed after field works, reviewing the available reports and maps from the area, and then, the satellite data were classified. Temporal analysis of the satellite images showed that by doubling the irrigation water share during 14 years, soils with severe and no salinity risk were decreased by 5 and 16%, respectively, while 20% was added to the land with moderately salinity risk. During this time the area of waterlogged lands has been doubled. The images of 1990 showed that new waterlogged lands were developed in the vicinity of the drainage and irrigation canals.

Remote sensing has been considered as an appropriate tool for temporal monitoring of some natural phenomena. Ardestan Region is prone to land degradation and masked by sand sheets, sand dunes, clay flats, desert pavement and different kinds of salt crust due to dry climate. To study the trends of land degradation in last three decades, four satellite data sets of LANDSAT MSS, LANDSAT TM, LANDSAT ETM + and IRS acquired in 1976, 1990, 2001 and 2008, respectively were analyzed. The time series analysis revealed that the bare clayflats have decreased and clayflats with vegetation cover have expanded over 32 years. During this period, the areas which are covered by gravel have decreased 13 percent and both the area covered by salt crusts and aeolians have extended 2 percent. Puffy grounds have developed by 2001 but their magnitudes have decreased between 2001 and 2008 as they have been masked by the moving sand ripples. Reduction of 13 percent of sand sheets between 1990 and 2008 indicates that soil conservation practices have efficiently controlled land degradation and desertification in the area.

Introduction: The idea of ecosystem health assessment (EHA) was introduced in environmental management in the late 1980s. In the discussion of health assessment, indicators are raised. Indicators provide a better picture of the environment Ecological indicators are committees that are closely related to the complex characteristics of the ecosystem. But these indicators are often not directly measurable. Measuring these indicators is used to simplify and evaluate various aspects of ecosystem performance. Examples of these indicators are: Vegetation density index, fold index, continuity index, Euclidean distance or distance from the nearest neighbor, environment to area ratio and spot area. The aim is to prepare a health map of the study area. In this process, after detecting changes, the thresholds of health, confusion, and disorder are determined. And health disruptors are identified. At the end, health diagnosis instructions are provided. The main question of the research is whether the health of the land has changed in the desired period of time? Materials and Methods: The study area in this study is sub-basins of Qarahsoo, Nekarood and Gorganrood watersheds including Gorgan, Kordkuy and Bandar Gaz counties. The health utility map was obtained using the weighted linear combination (WLC) method. Thus, the health maps of 1984, 2000 and 2018 were prepared. By comparing the three maps, health changes in the study period were examined. In these layers, more desirability indicates a higher degree of power and less desirability indicates a lower degree of power for the health of the landscape. The number zero indicates the worst state of health and the number 255 indicates the best state of health in this study. In the next step, the health threshold is determined and also the health disruptors (such as human development and diseases) that are available and changeable are identified. Discussion of Results: At this stage, land health maps have been prepared using quantitative indicators it is presented as a map. Using the health maps obtained from the previous stage and the resulting change map, the health changes of the landscape were compared statistically and visually. At this stage, the health map changes of 1984 and 2018 were prepared and classified. Areas that were unchanged from the base or had a slight change their health condition was considered excellent. Areas with low, medium, and high variation were considered good, moderate, and poor in health, respectively. In this section, changes in the landscape of the land from 1984 to 2018 were examined. From the point of view of the criteria studied in this research, in this 32-year period, the least changes and consequently the best state of health are related to 1984. Therefore, the 1984 health map was considered as the basis and other years were measured and compared accordingly. Changes in the health map of 1984, 2000 and 2018 were examined. Changes between 2000 and 2018 are negligible. Using the prepared health maps, the health changes of the land appearance between 1984 and 2018 were mapped. In the resulting change map, which was considered in the range of 0 to 255, Trial and error showed that up to 130, the changes in the study area are insignificant. Changes in the region are significant from 130 onwards. The number 130 was considered as the health threshold of the land. Therefore in addition to presenting changes in the landscape of the land, areas without change that did not cross the health threshold Changed areas that have crossed the health threshold are also shown. The development of human land uses such as urban development and roads, as well as the conversion of land uses such as forest to agriculture, rangeland and roads, runoff and erosion are introduced as factors of change. These cases may also reduce the area of forests, diseases and climate. Figure of landscape health final map of the study area presented in below: Figure of landscape health final map of the study area Landscape health changes in the studied time periods were evaluated and compared using measures. The results in the mentioned period show the declining trend of the health of the landscape. Examination of the results shows that the uses are more uneven and the damage to the landscape has increased. Due to the increase in fragmentation index and decrease in integration and communication, the environmental situation has declined. Due to the high capability of satellite images-such as timeliness, multi-spectrum, duplication-they can be used to determine changes in the landscape in a certain period of time. Using the landform measurements, the spatial structure of the land landscape can be quantified. By establishing a relationship between the structure and performance of the landscape and a better understanding of ecological processes, it is possible to evaluate the landscape in order to plan and manage it sustainably. As a result, the use of metrics, while saving time, provides acceptable results. The measurements can be studied and extracted as quantitative indices of the environment using satellite images. The larger the area of the spots, the less damaged and intact they are. The shorter the distance between the stains, the less tampering. Therefore, closer distance is a favorable factor in the health status of the land. Maintaining the integrity and stability of the landscape based on ecological principles leads to reducing or improving the effect of human activities on biodiversity and the dynamics of local landscapes. In the discussion of detection of changes, the measurement of land use is one of the most telling measures in the study of changes in the appearance of the land. In this study, in addition to visual analysis, cross-books were used to understand where it has changed and how much. In terms of area, 2000 is not much different from 1984. In 2018, in terms of area, about 4, 000 hectares were added to the development area, including the city and roads. The area of the city has tripled compared to the base year. About 26, 000 hectares of forest area has been reduced. The rate of erosion has increased by about 40 hectares (increase of run off). About 9, 000 hectares have been added to the area of agricultural land. About 12, 000 hectares have been added to the rangeland area. The area of roads has increased by 800 hectares. The 1984 health map is in a better position than the 2000s and 2018s in terms of the indicators studied. In the map of 2000, compared to 1984, the situation of forests, pastures and agriculture has deteriorated. In the 2018 map, the situation of forests and agriculture has deteriorated, but the situation of pastures is acceptable. The number of urban spots has increased. At the city level, there are slight positive changes due to the large and concentrated spots in the city. The maps for 2000 and 2018 do not show much difference from each other. But they are a significant change from the 1984 map. Statistical analysis of the histogram shows that the 1984 curve is more homogeneous than 2000 and 2018. The 2000 and 2018 curves are closely related. Landscape changes from 1984 to 2018 were examined. From the point of view of the criteria studied in this research, 1984 is in a better health condition than other years. Therefore, it was considered as the basis and threshold of health and other years were measured and compared accordingly. In line with the findings of this study and their ecological analysis, guidelines for diagnosing the health of the landscape were presented.

Introduction One of the most important planning tools for timely supply of crops, especially the strategic wheat product, is to predict the performance of this product before harvest, which can be very important in planning for itself. Combining the results of observations and ground measurements with remote sensing techniques can be widely applied in all agricultural sectors and facilitate the access to precision farming. Agricultural products have always been associated with the risk of fluctuations in the climate and changes in international markets, although this risk is never completely eliminated, but it can be understood by identifying the various parameters affecting plant growth and estimating the amount of the product Before harvesting, they minimize them. The forecast of rainfed wheat yields as a strategic product, with the Earth's population reaching 7 billion now. Materials and methods Field data includes biomass and net weight of wheat produced per farm in kilograms. These data are obtained by direct field surveys during harvesting. The GPS was used to determine the total area of the land, and considering the time zone of the crop, the LANDSAT-8 satellite time series was used from mid-February to late May in the studied years. After performing the necessary pre-processing on the images, the images were classified using a multi-timed classification. Initially, both NDVI and LAI indexes were obtained for all images in each ENVI environment every 5 years. Finally, the phenolic curves of both indices for each plot of land were fitted for each year from the studied years, which cultivated the wheat field, and the time of each phonological step was obtained for the studied area. Results In order to evaluate the overall accuracy of the classification, the Kappa coefficient and overall accuracy for the classes defined separately were calculated using the classification error matrix. According to the phenological diagrams, the parameters of the area under the charts of both indicators were calculated for all lands. According to the phenological diagrams, the parameters of the area under the charts of both indicators were calculated for all lands. For this reason regression relations and determination coefficients (R2) between indices and wheat and biomass were created. For this reason regression relations and determination coefficients (R2) between indices and wheat and biomass were created. In this study, both indicators were affected by the multivariable regression of the product estimation, and the highest coefficient of determination was obtained for each of the indices alone. From the 5 phonological stages, the inflorescence stage with R2=0. 65 has the highest correlation coefficient. Discussion and conclusion From the obtained coefficients, we conclude that GLAI or green leaf area index (absorption) has a higher coefficient than NDVI. GLAI, which represents the main part of the photosynthesis of the plant (leaf), which is the main factor in the production process in the plant, certainly has a greater impact on the plant's production process. This leads to the preference of this index for the NDVI for estimation, but since the main goal of the paper is to obtain a multivariate regression relationship, we can do this in addition to the effect of both the desired index, the coefficient of determination and continuity For each of the indicators, we increase individually and make estimates in the region more accurately. With the involvement of both indicators in our relationship, we obtained a significant coefficient, especially for biomass with R2=0. 865, which ensures that our prediction values are close to real values and that the program On the basis of this estimate, the probability of success will be high. From the study of phonological stages with wheat yield, we also conclude that, firstly, the entire phonological stages have less regurgitation coefficients than the phonological graphs of the two vegetation indexes, which means the whole diagram of wheat growth stages relative to the phenological periods on these graphs Have more ability to estimate the yield of wheat. Two of the five phonological stages studied, the inflorescence formation stage with R2=0. 65 the highest correlation coefficient. This step in time is about the peak of the graph or the maximum value of the phonological graph.

Conventional methods use point measurements for estimating evapotranspiration,however, in remote sensing techniques, such as Surface Energy Balance Algorithm, the instantaneous evapotranspiration flux during satellite transit is calculated as the remainder of the equation in the form of energy balance for each pixel. In this study, two standard mono-source evapotranspiration models were estimated from SEBAL and PYSEBAL and were compared with the results of a drainage lysimeter planted with grass in the Qazvin plain. Satellite data were based on the data from three sensors, MODIS, LANDSAT-5-TM, and LANDSAT-7-ETM, from 2000 to 2003. The results of this study showed that the PYSEBAL model in all three sensors with RMSE (0. 45, 0. 46, and 2. 02 mm/day) respectively had better performance than the SEBAL model. Also, the studies performed from the three sensors showed that the MODIS sensor with standard error value (0. 15 mm / day) and correlation coefficient (0. 98) compared to the two ETM and TM sensors with correlation coefficient values (0. 97 and 0. 53), standard error (0. 17 and 2. 26 mm/day) as well as higher spatial resolution have been able to produce better results.

BACKGROUND AND OBJECTIVES: Evapotranspiration is an important component of water balance associated with the hydrological cycle and biological processes. Accurately estimating the rate of evapotranspiration is crucial for understanding fluctuations in water availability and effectively managing water resources in a sustainable manner. The study aims to examine the correlation between actual evapotranspiration and potential evapotranspiration by assessing the linkages with vegetation and snow cover in an ecologically fragile located in the northwestern Himalaya.MATERIALS AND METHODS: The present study uses remote sensing LANDSAT satellite data series to map vegetation cover and snow cover in the area. Remote sensing data accessed from Moderate Resolution Imaging Radiometer evapotranspiration project data was used for calculating evapotranspiration and potential evaporation. The data from the Climatic Research Unit (2000–2022) was additionally utilized for the computation of potential evapotranspiration. The study investigates variances in evapotranspiration and explores correlations between normalized difference vegetation index and normalized difference snow index. It further examines the correlation between potential evapotranspiration and actual evapotranspiration.FINDINGS: The study conducted from 1991 to 2021 demonstrates a notable rise in vegetation cover by 20.18 percent, showcasing spatial variations across the region. Conversely, there has been a significant decline in the extent of snow cover throughout this period. A positive correlation was identified between vegetation cover and evapotranspiration, whereas a negative correlation was observed between snow cover and evapotranspiration. Actual evapotranspiration is on the rise while potential evapotranspiration is declining throughout the region.CONCLUSION: Hydrological cycle of a region is governed by many factors such as climate (precipitation, temperature), geohydrology, land use and land cover, socio-economic condition of habitants and institutions. Vegetation cover, snow cover, actual evapotranspiration and potential evapotranspiration and their relationship indicates changes in local and regional climate. An incremental rise in plant growth across the study site, coupled with spatial variability and a reduction in snow cover in the elevated mountainous zone, is influencing both actual evapotranspiration and potential evapotranspiration. Increase in actual evapotranspiration in the High Himalayan area of Himachal Pradesh attribute to substantial increase in vegetation cover in the dry cold desert region. The findings of the study will contribute to the comprehension of essential elements of water cycles and water budgets, facilitating improved resource allocation for climate-resilient sustainable initiatives.