Background and Aim: Fluorscence Tomography is Emerging as an important alternative to molecular imaging. Simultaneous detection of several biological processes in vivo is a common requirement in molecular imaging. Application of multiple fluorophores and Multispectral approaches have been widely used for distinguishing fluorescence signals. But, in case of spectral overlapping isolation of signals requires using unmixing algorithms. In this study, the researchers used blind source unmixing method, Non-Negative Matrix Factorization (NMF), to decompose contributions of different fluorescent probes in Fluorescence Molecular Tomography (FMT) images.Methods: A cylinder tissue-like phantom containing 2 transparent tube with 0.5 millimeter inner diameter filled with different fluorophores (fluorescine and rhodamine). It was imaged by using experimental FMT setup at three different wavelengths. We performed NMF algorithm on Multispectral measurement to resolve the relative contributions of fluorescent dyes. Multispectral projection was simulated and used as input of unmixing algorithms in simulation part of study. The projection images of each probes was simulated and used as reference data to evaluate performance of unmixing algorithm for simulated images.Results: Two set of data, each of them corresponded to isolated contribution of each fluorophore, were obtained as the result of performing the algorithm on Multispectral measurements. The "root mean square error" (RMSE) was calculated between original reference and related unmixed images. The difference between "Peak signal to noise ratio" (PSNR) values was statistically significant for each reference image (p<0.01). It shows that the proposed algorithm works in an accurate manner. The correlation coefficient between unmixed images and related reference image was more than 97% in simulation study. Unmixing method based on NMF can be used as an efficient technique to unmix contribution of different florophores. The 3-D location of each florophore can be obtained separately by reconstructing of these unmixed image.

Detailed information on forest combination is required for many environmental, monitoring, and forest protection purposes. The link between ecology and remote sensing provides valuable information for the study of forest trees to facilitate the study of ecosystem performance and to measure the spatial distribution of vegetation. In recent years, the use of modern remote sensing methods and techniques based on UAVs have been used for regular updating of forest inventory. In this research, different data sources including multi-spectral and RGB images with very high spatial resolution, were used for tree species recognition in plain forests of Noor City located in Mazandaran province. Also, taking images was performed in the growing season to prepare a time series of UAV-RGB images for investigating the effect of tree crown phonological changes on classification accuracy.  Following orthomosaic generation, RGB (NGB, NRB) and multi-spectral (NDVI, CIgreen) indices were calculated and the random forest classification method was used for forest species classification. Based on single-time images, late April images provided the highest overall accuracy (75%). However, the results of the time series obtained from RGB images showed an increase in accuracy of up to 86%. Species identification based on Multispectral images obtained from the Sequoia sensor also provided 85% accuracy. The results showed that the single-time image at the appropriate time using a UAV-RGB, compared to taking a time series and using a UAV equipped with Multispectral sensors, has acceptable and less expensive results for tree recognition in the study area.

Introduction Land use change reporting over time is necessary to assess and monitor the state of natural and agricultural resources. Knowing about the change of use is necessary to identify the priorities of public investment in the management of natural resources and to evaluate its effectiveness. The purpose of land change investigation is land use management. Among the management cases, we can mention the evaluation of the effect of economic activities and development on the environment. In such cases, these organized reports are the best sources of decision-making. Land use management can ensure that resources are used efficiently and people's future resources are preserved. This process is the main component of a development plan. Timely and accurate detection of land use changes is the basis for a better understanding of the relationships and interactions between humans and natural phenomena, and as a result, provides better management and more appropriate use of natural resources. Satellite images as a type of remote sensing data are well used in the field of natural sciences for quantitative and qualitative measurement of land cover changes. The construction of the Houzian dam in 2015 and also the expansion of mines and construction in the natural resources of Aligudarz city in Lorestan province and also the lack of accurate statistics on the amount of land cover/use changes in the region make such research necessary. In the present study, land use changes in Aligudarz city were investigated during nine years for the years 2012 and 2014 with the help of multi-spectral satellite images and the artificial neural network. In the structure of the artificial neural network, numerous nodes work together in parallel with the purpose of processing. Each node is a data structure. This data structure is placed in a communication network with each other and the network is taught by humans. Materials and Methods In this study, several key steps were used to prepare and identify LULC changes in Aligudarz city, which include data pre-processing, image processing and classification implementation as well as validation. The required images were selected among the available images in such a way that they have minimum cloud cover and maximum greenness in the plants and trees in the area, and the date of the images are related to the same month. This study uses land use change detection in the east of Aligudarz county using Landsat 8 OLI and TIRS images. The spatial resolution of these images was improved to 15 m using the fusion technique and panchromatic band. At first, preliminary pre-processing including radiometric, atmospheric, and geometric corrections were done on the raw data. The geometric correction was done with the RMS square root error of 0.22 pixels. Radiometric and atmospheric corrections were done in ENVI 5.3 software using Radiometric Calibration and Quick Atmospheric Correction tools. The artificial neural network method was used to prepare land use maps for 2013 and 2021. The neural network structure used in this research is a three-layer perceptron neural network, which includes seven input neurons (number of satellite image bands), eleven intermediate neurons, and six output neurons (number of land cover map classes). The classification accuracy was evaluated quantitatively by comparing the LULC classes obtained from the training phase with the data obtained from the testing phase. The classification accuracy was evaluated quantitatively by comparing the LULC classes obtained from the training phase with the data obtained from the testing phase. In this study, the points taken from the ground surface and Google Earth Pro 7.3.4.8642, using the error matrix and the Confusion Matrix Using Ground Truth ROIs tool were used. Detection of changes between two classified maps was done with Change Detection Statistics and Workflow Change Image and Spear Change Detection tools. Results and Discussion The results of this study showed that the artificial neural network has an acceptable performance in investigating land use changes and, The Kappa coefficient for 2013 and 2021 was 0.83 and 0.71%, respectively. Due to the construction of Houzian Dam in 2016, water areas have witnessed an increase of 1.34%. Also, the construction of the dam has led to an increase in the area under irrigated cultivation, so the area under cultivation in 2021 experienced an increase of 5.53% compared to 2013. In addition, the construction of the dam has caused the highlands to decrease by 4.30 %. Because the water of the dam has been used to irrigate the highlands where there was not enough water to irrigate them before the construction of the dam. The area of mines has increased by 0.23% during the studied period. The area of uncovered regions has decreased by 1.74% compared to 2013. Also, the area of habitation regions has decreased by 1.06% to 18.18 square kilometers in 2021. Conclusion The survey of the land use map of Aligudarz showed that the heights and water areas have the largest and smallest areas, respectively. The results of this study showed that in the years after the construction of Houzian dam compared to before its construction. The total area of water and total vegetation has increased. Since the construction of a dam in an area has short-term and long-term effects, it should be noted that the increase in vegetation and the cultivated area is considered a short-term effect. Therefore, it is necessary to investigate the impact of creating this water structure in the region's ecosystem in the long term by forecasting models. In the investigation of mines, the appearance of water areas indicates an increase in the depth of excavation. Since this city is an important center for stone production, the absence of a specialized regulatory body on the number of harvests and the impact of mining on the environment is felt in this region. Another part of the increase in water areas is due to the existence of errors in the classification of land use in the artificial neural network. In using the results of this research, it is important to mention that these results were obtained for the area of the dam and the increase in vegetation caused by the construction of the dam cannot be generalized to the entire basin.

Background and Purpose: Agriculture serves as the cornerstone of the global economy, providing the main source of food and raw materials for various industries. However, the rising demand for food as a consequence of population growth represents a considerable threat to food security, particularly in light of the limited access to freshwater resources. It is noteworthy that agriculture alone consumes about 70% of the world's freshwater resources, thereby emphasizing the critical need to manage and enhance irrigation efficiency to ensure sustainable food production. Therefore, the management and enhancement of irrigation efficiency are essential. At the core of determining irrigation water requirements lies the concept of actual crop evapotranspiration (ETa), which represents the combined water loss from soil evaporation and plant transpiration. Accurate estimation of ETa is crucial in optimizing irrigation methods, maximizing crop yield, and minimizing water consumption. Various models and tools have been developed to estimate ETa, aiming to provide more user-friendly and efficient methods for farmers and researchers. Given the extensive application of ET estimation models, there is a clear need to focus on the development of accurate and efficient methods for determining this parameter. Thus, this study aims to compare user-friendly ETa estimation methods, including the EEFLUX system, the METRICTOOL tool, and the automatic hot and cold pixel selection method of the SEBAL and METRIC models.Materials and Methods: The Earth Engine Evapotranspiration Flux (EEFLUX) is a version of the METRIC model that operates on the Google Earth Engine platform. METRICTOOL is a new tool in ArcGIS based on the METRIC model, offering enhanced pre-processing capabilities and automatic data identification. This tool reduces computation time by 50% and provides a user-friendly alternative to other existing METRIC model implementation platforms. The automatic hot and cold pixel selection method involves creating a binary map of eligible pixels using a rule-based classifier and a comprehensive search algorithm to identify hot and cold pixels based on defined criteria. To estimate ET using these methods, six Landsat 8 satellite images were utilized during the winter wheat crop planting period at Tehran University farms in Mohammadshahr Karaj. The evaluation of these methods was conducted using alfalfa reference evapotranspiration (ETr) calculated with the FAO-Penman-Monteith method as reference data.Results and Discussion: The Root Mean Square Error (RMSE) values for the EEFLUX system, METRICTOOL, SEBAL, and automatic METRIC tools were determined as 2.45, 0.33, 0.39, and 2.76, respectively. Despite numerical differences, the evaporation and transpiration product of the EEFLUX system showed significant correlations with other methods. For instance, the R2 between ETa estimates from the EEFLUX system and the METRICTOOL tool was found to be 0.91. Although the data from the EEFLUX system may not be precise enough for local studies due to the use of CFSV2 global meteorological data in Iran, they yield acceptable results in large or global-scale studies. The METRICTOOL tool and automatic METRIC model exhibited the highest correlation (R2=0.99) and numerical agreement with each other, with RMSE values of 0.33 and 0.39, respectively, indicating higher accuracy compared to the automatic SEBAL model.Conclusion: The results of the numerical analysis indicate that the automatic hot and cold pixel selection approach can achieve similar accuracy to that of the METRICTOOL tool. This automated approach enhances the efficiency of the model in terms of time and effectiveness, reducing the potential for human error in estimating evapotranspiration for new or inexperienced users, and making these models accessible to the public. Furthermore, EEFLUX data can be utilised for the implementation of management measures in large-scale studies.

Road detection and Extraction is one of the most important issues in photogrammetry, remote sensing and machine vision. A great deal of research has been done in this area based on Multispectral images, which are mostly relatively good results. In this paper, a novel automated and hierarchical object-based method for detecting and extracting of roads is proposed. This research is based on the Mean Shift Segmentation Method and the HSI Color Model for Road Detection. Initially, the Multispectral images were segmented and then NDVI and NDWI spectral indices were created. In addition, the segmented images were transformed to HSI color space. Then, primary road surfaces were detected by Hue, NDVI, and NDWI spectral indices. In addition, the centerlines of roads were extracted using Voronoi diagram-based technique. After extracting of centerlines of primary roads, dangle errors were removed with emphasis on the topological rules and the lengths of dangles. In order to evaluate the proposed method, the Moonah multi-spectral Image provided by the ISPRS was used. According to the evaluation results, the parameters of completeness, accuracy and quality of the proposed method are, on average, estimated to be 98%, 84% and 84%. In addition, the results of the proposed method were compared with the results of five state of-the-art methods. The results demonstrate the high capability of the proposed method in detecting and extracting roads from satellite Multispectral images in urban areas.

Water stress occurs as a result of the imbalance between soil water in the root zone and plant water use, which necessitates determining the water stress index of the plant. Surface soil moisture is directly related to plant water content. Availability of satellite data has led to temporal and spatial resolution of field data and offers new opportunities for monitoring crop conditions. In this research, accurate and continuous monitoring of soil moisture content, as a representative of soil moisture stress, was done with field measurements of soil moisture, and comparison with Multispectral data of Landsat 9 and Sentinel 2 satellite images. The relationship between plant indices, as an independent variable, and soil surface moisture, as a dependent variable, was studied using linear multivariate regression and M5 tree regression methods. Considering the non-linearity of the relationship between soil moisture and spectral reflectance, linear multivariate regression did not show satisfactory results with coefficient of determination (R2) of 0.46 and 0.34 for Landsat 9 and Sentinel 2 satellites, respectively, as well as the root mean square error (RMSE) equal to 0.043 and 0.052. However, M5 tree regression showed more acceptable results, such that by establishing 16 and 20 regression relationships for Landsat 9 and Sentinel 2 satellites, the soil moisture was estimated withR2 of 0.70 and 0.67 and RMSE of 0.033 and 0.038, respectively. The results showed that the estimation of soil moisture with methods based on machine learning, such as the M5 model, increases the accuracy of calculations. In the M5 decision tree regression, a high number of variables does not necessarily lead to an increase in the accuracy of soil moisture estimation, and a relationship with the highest accuracy was found in the low number of variables. Therefore, the relationship obtained at the field level can be used to evaluate soil water stress and determine irrigation time in agricultural lands on a large scale, without measuring soil data.