Journal of Nature and Spatial Sciences
Volume:2 Issue: 1, Winter and Spring 2022

Nitrogen and phosphorus yields in watersheds can be influenced by air temperature and precipitation. Rising air temperature can affect the natural nutrient cycling process since most nutrient cycles are temperature-dependent. It is hypothesized that increasing air temperature and precipitation can alter the hydrologic cycle and impact the yield of both nitrogen and phosphorus in watersheds. This research's primary objective is to evaluate the influence of climate and land use characteristics of the watershed on nutrient loads in New England watersheds.

Nutrient data from the Spatially Referenced Regression on Watershed Attributes (SPARROW) model, land use data came from National Land Cover Database (NLCD), temperature and precipitation from USGS are used in statistical analysis with univariate and robust regression functions. The scatter analysis shows that nitrogen and phosphorus have more variability at higher temperatures.

Nitrogen and phosphorus have more variation at mid-range precipitation levels and are more diluted with higher precipitation. Robust regression results found that temperature and agricultural land significantly affect nitrogen yields in streams. Temperature, forested land, and agricultural land have the most significant impact on phosphorus in streams. Nutrient management is suggested to target areas to increase watershed resilience to climate change.

 Chenopodiaceae species are important vegetation around the world, especially in the desert and semi-desert areas. Preserving the biodiversity of Chenopodiaceae species is crucial to preventing soil erosion. In addition, most of them are of ecological and economic importance and also play an important role in biodiversity around the world. Conservation of this biodiversity is vital to the survival and sustainability of the ecosystem. To protect plant biodiversity, it is essential to know the plant species in their natural habitats. Therefore, automatic identification of plant species in their habitat helps to analyze the species and thus take care of their biodiversity. Computer vision approaches can be used to automatically identify and classify plant species. Modern approaches use deep learning in computer vision.

  In this study, the ACHENY data set that consists of 27030 images of 30 species of Chenopodiaceae are used. Firstly, using the SuperPixel method, larger size images (448×448) than existing ACHENY dataset images size (224×224) are created.  Secondly, based on the newly created dataset we introduce a proper deep learning model to identify Chenopodiaceae species.

 The results of the evaluation confirm the improvement of the classification accuracy of ACHENY species by the proposed model compared to the previously presented models. The results of the experiments indicate a superiority of about 3% accuracy of the proposed method and all evaluation parameters of the research have increased to a reasonable extent.

 The rapid growth of the world's population is a factor in focusing on smart cities; the smart city's ultimate goal is to achieve a clean, healthy, safe, and sustainable city. The biggest challenge of smart city policies is to consider the low importance of smart urban growth indicators. Given that several plans are currently being implemented to make Yazd smart, it is necessary to develop smart urban growth indicators to achieve this goal. The Yazd can be known as a city with the opportunity and potential for smart growth by considering its unique architecture and the people's lives model from the past until now; the history of this goes back to the distant past The purpose of the present study was to evaluate the indicators of smart cities in regions of Yazd. This research is practical in terms of purpose

The method of data collection is through a questionnaire. The statistical population was Yazd citizens, and according to Cochran's formula, it was 384 people. Data are analyzed using AHP techniques. The research results show that the regions of Yazd are in a different situation in terms of smart city indicators.

 The important principles are the difference of smart-making indicators in the five regions of Yazd, so that region 3 with a numerical 0.415 is in the first rank and region 2 with a numerical 0.148  in the fifth rank and in the most unfavorable conditions of urban smart growth indicators Hooshmand that needs to study the regions to remove obstacles and limitations and use the opportunities according to the level of indicators in the regions

 Predicting and studying the trend of climate variables in the future plays an important role in the optimal management of water resources. Different methods are used to determine the trend of change. One of the most common methods of trend change analysis is time series analysis. Time series is a set of observations about a variable that is measured at discrete points in time, usually at equal distances, and arranged in chronological order

  In the present study, the trend of precipitation changes in Dezful plain during 32 years was investigated and by selecting the appropriate time series model, a forecast was made for the next ten years. Man-Kendall’s non-parametric test was used to investigate the trend of precipitation changes.

 The result of this test showed that the annual precipitation of Dezful had a decreasing trend due to having a Man-Kendall statistic of -1.6. To select the appropriate time series model, data preparation (trend elimination and normalization) was performed first. Data stagnation was assessed with autocorrelation (ACF) and partial autocorrelation (PACF) charts. Using the differentiation method, the data became static (eliminating the mean trend) by applying one-time differentiation. By static data, random models were used to predict the average annual precipitation. Then, by fitting different Arima models and considering the criteria of T, P-VALUE less than 0.05 and Bayesian information criterion (BIC), the Arima model (3,1,1) was selected as the most appropriate model and to verify this the model was predicted for the period 2011 to 2018. The validation results showed that the prediction of this model is acceptable according to the actual values. Then, based on this model, a forecast was made for the next ten years from 2019 to 2028, which is predicted that the precipitation trend will decrease for the next period.

There are several methods of measuring desert pavement roughness. Among these methods, one can name laser and sonic rangefinder, 3D photography, and close-range photogrammetry. Remote sensing techniques need less and cheaper equipment than laser and sonic methods. In short-range photogrammetry, the quantitative amount of terrains can be obtained by processing the images of a digital camera using special methods of photography and camera calibration.

  This method can be introduced as an accurate and cost-effective measuring method to provide a digital model of complications and a three-dimensional model of objects. The present study aimed to evaluate the possibility of using close-range photogrammetry in measuring desert pavement roughness. In this research, first, the calibration parameter of the camera was calculated by taking photos of standard patterns. Then, the meshed samples of desert pavement were photographed and the photos were three-dimensionally simulated.

 The results showed that since in this method the selected points have more effective height and uniform dispersion, the measurement of the average height of roughness is more accurate. It means that measuring the roughness of the soil surface is done with high accuracy in a short time.

 Temporal-spatial changes in climate parameters, especially temperature, are considered one of the most obvious signs of climate change in a region. The aim of this study was to investigate the average temperature changes in the Abarku-Sirjan basin.

  In this regard, the daily analyzed data of ERA-Interim with a resolution of 0.25 * 0.25 degrees during the period 1979-2019 were used. According to the dimensions of the studied data, 338 points covered the whole basin. The trend of the studied data was examined using the Mann-Kendall test. Hot spots analysis was then performed on them.

 The results showed that the temperature has an increasing trend in most months of the year. In April, May, August, and December, some parts of the basin have no trend and the rest of the basin has an increasing trend. In general, no decreasing trend has occurred in any part of the basin during the study period. Hot spot analysis also showed that the northwest of the basin has cold spots and the south of the basin has hot spots. In general, in the basin, hot spots are more frequent in the warm months of the year and cold spots are more prevalent in cold months of the year.