BACKGROUND AND OBJECTIVES: Urban heat island is characterized by higher temperatures in urban areas compared to their surroundings. Vegetation, quantified by the normalized difference vegetation index, is key in mitigating urban heat island effects and influencing land surface temperature. With the rise of Machine Learning techniques, particularly random forest, land surface temperature predictions have become more accurate. This study explores alternative normalized difference vegetation index adjustments to understand their impact on urban heat island classification in Chiang Mai, Thailand. It investigates how changes to the normalized difference vegetation index can help to be part of practical urban planning measures, such as prioritizing vegetation type and location for cooling strategies in urban areas. Furthermore, the study aims to highlight the importance of vegetation as a sustainable solution for mitigating the adverse effects of urban heat island and enhancing urban livability.METHODS: Satellite data from Sentinel-2 and Landsat 8 for 2016–2022 were used to develop a 20-meters grid resolution dataset, resulting in approximately 2 million points. Random Forest was employed to predict land surface temperature, followed by systematically adjusting normalized difference vegetation index values from -100 percent to +100 percent in 10 percent increments. Urban heat island was classified based on standard deviation thresholds. The results were analyzed and compared visually using geographic information system, incorporating spatial variations and heat intensity patterns to better understand the urban landscape.FINDINGS: Adjusting normalized difference vegetation index values showed a nonlinear relationship with Land Surface Temperature predictions, where certain thresholds caused unexpected decreases in Land Surface Temperature. Urban heat island classifications identified distinct urban regions with varying heat intensities. The visual comparison highlighted significant differences between the base case and alternative scenarios, revealing the sensitivity of land surface temperature to vegetation density. the results also emphasized the role of high normalized difference vegetation index values in cooling urban regions and reducing urban heat island intensity, while extreme reductions in vegetation led to potential misclassification of water bodies, creating anomalies in cooling patterns. The results of this research provide information on important variables that affect the changes in the urban heat islands, focusing on changes in vegetation, which can be a part of decision-making to improve urban planning in the future.CONCLUSION: The study demonstrates the influence of normalized difference vegetation index on urban heat island classification and its potential in urban planning strategies. By highlighting nonlinear trends, the research underscores further the need to explore vegetation dynamics in land surface temperature predictions. The findings contribute to a deeper understanding of urban heat island effects and provide a basis for enhancing machine Learning models and urban planning frameworks. Future studies could expand to other urban areas, incorporate additional variables, and refine predictive algorithms for broader applications. This study will serve as a foundation for the development of future real-time monitoring tools that will enable proactive and sustainable solutions to UHI problems. 

BACKGROUND AND OBJECTIVES: The condition of the Bengkulu watershed area is outlined in the Indonesia Integrated Watershed Management Plan. Adverse conditions in the watershed have been linked to a range of natural calamities, such as floods and droughts. Moreover, the process of converting forests into plantations or agricultural lands has resulted in environmental degradation. Therefore, there is a pressing need for an evaluation of vegetation density and plant analysis within the watershed. This is crucial for comprehending ecological conditions, devising restoration measures, and implementing conservation efforts. Hence, the aim of this study is to analyze vegetation density and track plant diversity, specifically focusing on tree characteristics, throughout.METHODS: The study techniques utilized in this investigation encompass the gathering of normalized difference vegetation index data from satellite imagery, followed by its analysis through the utilization of geographic information system software. Sentinel satellite imagery from 2021 is utilized due to its efficacy in monitoring environmental conditions and managing natural resources. Spatial data encompass maps and field data.FINDING: By employing normalized difference vegetation index data, the study pioneers a novel approach to environmental monitoring, setting an example for effective resource management and ecological conservation in watershed regions. The study findings indicate that 29 percent of the watershed area exhibits moderately steep topography with a dendritic flow pattern. The assessment of the normalized difference vegetation index demonstrates that the watershed is comprised of multiple sections abundant in high-density vegetation, primarily dedicated to plantations. Within the Bengkulu watershed area, a total of 49 tree species from 22 families were identified, with diversity indices falling within the moderate category.CONCLUSION: An in-depth knowledge of the ecological factors and plant preservation initiatives in the Bengkulu watershed can greatly aid in sustainable environmental management and help policymakers develop more effective policies for ensuring environmental sustainability. The findings of this study contribute significantly to the Global Journal of Environmental Science and Management’s goals of promoting sustainable environmental management and biodiversity preservation, offering actionable insights for policymakers and conservationists.

Background and Objectives: Soil moisture is one of the key variables which by controlling evapotranspiration processes influences the water cycle and heat exchange between the earth and the atmosphere. The amount of soil moisture is also important for hydrological, biological and biochemical cycles. With the help of soil moisture information in regular intervals, the degree of drought development can be determined in regions with dry climates. Furthermore, continuous monitoring of soil moisture in agricultural areas can help to plan irrigation of crops effectively. Soil moisture is also used to identify areas susceptible to fire in forest areas. Therefore, monitoring of soil moisture is important in any regions and different time periods. Due to factors such as lack of uniformity in physical properties of soil, topography, land cover, evapotranspiration and rainfall, soil moisture is known as a variable factor in spatial and temporal intervals. Therefore, the use of conventional and traditional methods for soil moisture determination (such as gravimetric and neutron probe) is not appropriate to understand the spatial and temporal variation of this parameter in large scales. To resolve this problem in past two decades, remote sensing technology (especially in visible/infrared spectrum) widely used to estimate of soil moisture indirectly. The objective of this study was to estimate surface soil moisture using Normalized Difference Moisture Index (NDMI), Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST). Materials and Methods: For this purpose, Landsat 8 satellite imagery was downloaded at the same time as ground sampling. The samples were transferred to the laboratory and soil moisture was measured by weighted method. Then, using the expert software such as ArcGIS, the indices were estimated and the values of these indicators were transferred to SPSS software for statistical regression. In this study, a PTF were obtained to predict soil moisture condition using LST and NDVI and NDMI derived from Landsat 8 data. Multiple linear regression method was used to derive the PTF. After derivation of the pedotransfer function, the accuracy of the derived PTF was evaluated. This research was carried out in the Dehzad area of Izeh city of Khuzestan province. Results: Comparison between measured and predicted soil moisture values indicated that the PTF had good prediction (R2=0. 78), Coefficient of Residual Mass (CRM), Mean Absolute Error (MAE), Modified Coefficient Efficiency (E), Modified Index of agreement (d) also showed that the model had good performance (CRM=0. 001, MAE=0. 0013, E=0. 9998 and d=0. 9999). Furthermore, a soil moisture map was obtained for the study area. The result indicated that Normalized Difference Moisture Index (NDMI), Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) can be used to predict soil surface moisture content successfully. Conclusion: The result of this research has been presented by a PTF and in the form of soil moisture map. The soil moisture map simulated by this model can predict 78% of soil moisture variation in the region.

Introduction Field management is a part of precision agriculture (PA) which has positive environmental and economic effects on quality of plant productions. Nitrogen needs of plant, depends on climate conditions and growing pattern. The optimum of nitrogen fertilizer is varied from fields to fields. Nitrogen management causes uniform shape and size of potatoes, on the other hand decreases the inward and outward damages (Stark and Brown, 2003). Between different herbal indices, NDVI is the most common for monitoring greenness of plants. NDVI was calculated from reflectance in red and NIR bands (equation 1). Greenseeker (GS) is a suitable optical sensor because it is not affected by light and temperature variation or wind intensity. (1) In addition to GS, satellite image was used to evaluate the NDVI of studied potato field. Landsat 8 is the last satellite of this family with new sensors (operational land imager (OLI) and thermal infrared sensor (TIRs)) and additional spectral bands (deep blue invisible (430-450 nm) and shortwave infrared (1360-1390 nm). At the end, support vector regression (SVR) and principal component regression (PCR) or multi-linear regression (MLR) was applied to estimate RMSE and R2. The input of models was synoptic data, and NDVI extracted from GS or OLI. Materials and Methods The study was performed on marfona cultivar of potato field which located in Bahar city, Hamadan. The potato was planted early March and experiments were started after growing the first leaves. The soil texture in the experimented field was sandy loam soil to 75 cm depth. The territory (the southwest corner of the field) was fertigated by poultry manure with content 4. 5% of N in order to put shortage of nitrogen down. Metrology station of Bahar city reported the maximum, minimum and average temperature, relative humidity, precipitation and wind velocity which were effective on NDVI variation. The GS was put at a height of 60 cm above the plant and the average of NDVI was obtained by three times measurement. This sensor has red and NIR diodes which reflect and absorb the spectra in 660± 15nm and 770± 15nm regions, respectively. GS and OLI were applied for measurement every 8 and 16 days, respectively. Satellite images were analyzed two times (30cm height of plant and hilling stage) during the growing. Although, climate changing were effective on NDVI then some image corrections were necessary. Geometric and atmospheric corrections were applied for removing the absorption and distribution error with dark object subtraction and FLAASH algorithm in ENVI 5. 3 Software. In addition, GS is a nondestructive and contactless optic sensor which helps farmers to manage nitrogen because using laboratory method is not easy way for them. As well as, OLI provided accurate NDVI which support the accuracy of GS. Results and Discussion1 In order to correlate NDVI-GS and NDVI-OLI, the third parameter (INSEY) was explained. In season estimation of yield (INSEY) was estimated by dividing NDVI by days after planting (DAP). INSEY index is suitable to predict product potential performance. PCR and SVR methods in Matlab 2011b was used to calculated the relationship of INSEY and NDVI. Also, Red and NIR bands extracted from spectrometer (AvaSpec-ULS 2048-UV-VIS) in the 300-1100 nm region were used in order to support comparison of those sensors. Results showed that the reflectance spectra changed through the growing stage, which is logic because the size and number of leaves were increased and as a result the greenness was enhanced. NDVI calculated with spectra showed more accurate R2 for NDVI-GS (0. 94) than NDVI-OLI (0. 81). In addition, correlation coefficients of the SVR model between INSEY and NDVI were predicted 0. 947 and 0. 947 for the GS and OLI, respectively. Conclusions The result of the study confirmed the useful Greanseeker as an accurate and fast technology for prediction of NDVI. Among different regression methods, SVR showed the perfect results. Since the farm is a commercial one and not belong to the university, it would not possible to test different nitrogen fertilizer treatments. It is obvious that evaluation of field in different consecutive years helps us to codify manual fertilization.

Vegetation as a natural component plays a significant role in increasing permeability, improving soil, reducing evaporation, and reducing the runoff and thus reducing the possibility of flooding. The use of new technologies such as remote sensing and geographic information system to study plant ecosystems and prepare land cover maps is necessary to know the effectiveness of these tools and to identify the best methods of their use. The purpose of this research is to investigate the vegetation cover using the NDVI and compare the performance of three supervised classification methods, the maximum likelihood method, the minimum distance from the mean, and the parallelepiped method in a part of the Great Karun watershed. To this end, TM and ETM images of Landsat satellite were used in one interval and NDVI in a 10-year interval (May 2008 to May 2018) with the help of supervised classification and maximum likelihood algorithm. The above data were prepared and analyzed using ENVI4. 2 software, and the effectiveness of each method was evaluated with the overall accuracy index and Kappa coefficient. Based on the results in the maximum likelihood method, the overall accuracy rate is 90. 35% and the Kappa coefficient is 0. 878, in the minimum distance method, the distance from the mean is 74. 32% and its Kappa coefficient is 0. 675, and in the parallelepiped method, the overall accuracy is 67. 09% and the Kappa coefficient was calculated as 0. 593. Based on the results, the maximum likelihood method has the highest level of accuracy in satellite data group classification. Moreover, the results showed that in the 10-year period in Dez, Karun, and Karkheh watersheds, the spectral reflectance related to vegetation has decreased by 7. 4%, 10. 64%, and 13. 83%, respectively. The results of this research can be effective for the practical use of the analysis that was done in relation to the studies of runoff and flood. According to the process of vegetation changes due to natural or human factors, the need for proper management in this area seems necessary.

This study aimed first to investigate the relationship between Normalized Difference Vegetation Index (NDVI) and vegetation attributes (vegetation cover, bare soil, litter frequency, and the amount of biomass) and, then, evaluating the vegetation changes using NDVI in semi-arid rangeland in western Iran. Ground data were collected to assess the accuracy of NDVI index. For this purpose, 14 sampling units were randomly selected for collection of vegetation attributes including biomass, vegetation cover, litter, and bare soil. Then, the correlation between digital pixel values and the sampling units were analyzed. The results showed that NDVI was highly correlated with all vegetation attributes. The maximum correlation was related to vegetation cover (0.84). So, to evaluate the vegetation changes, the NDVI maps were created in 1986, 2001, and 2013. The results showed that the amount of class 1 (very poor vegetation cover) increased from 0.27 km2 in 1986 to 12.89 km2 in 2013, and also class 4 and 5 (good and very good vegetation cover, respectively) decreased about 27.8 and 37.7%, respectively. The relationship between precipitation and temperature with NDVI was investigated to assess the sensitivity of NDVI to these parameters. The results showed that the amount of precipitation decreased during the studied time periods. This parameter seems to be one of the most important factors affecting the vegetation in our study area.

Introduction Land surface temperature (LST) is controlled by the equilibrium of ground and atmosphere energy, as well as superficial and sub-surface thermal properties, and is considered as an important parameter in many environmental models. Knowing the extent of LST contributes to a wide range of issues related to earth sciences, such as the urban climate, global environmental changes, and the study of human-environment interactions. Land use and land cover information are recognized as an essential and important component of data used in various aspects of regional planning, research on global change, and applications in the field of environmental monitoring. On a global scale, changes in land use / land cover resulted in changes in regional and local temperature regimes. Land use patterns affect LST and can be considered as an indicator for the trend process. Using LST, everyone can find useful information about the physical and physical characteristics of the earth and climate that play a significant role in environmental processes. LST is an important factor in many fields of study such as global climate change, hydrology, agriculture, and land use / land cover. Methodology The aim of this study was the investigating the relationship between surface temperature and vegetation cover and land use in Gorgan plain using remote sensing data. In the first step, the Landsat 8 image of the year 2018 was pre-processed and prepared and the land use / land cover map was prepared in 8 classes. Then, to measure the surface temperature of the thermal bonding of the image and the related equations were used. Finally, the normalized difference vegetation index or NDVI was used to calculate surface mapping and the LST surface temperature map was created. In order to neutralize the effect of altitude on LST, selected pixels from the elevation points were selected in each land use. All LST computational steps and the NDVI index were performed using the ArcGIS10. 4. 1 software and the 2018 land use was created using the Idrisi software. In this research, the linear regression method was used to obtain the effect of NDVI and its effects on LST. Evaluation of LST extracted from meteorological stations It should be noted that the surface temperature, which indicates the surface heat of the body, is slightly different from that of the air contained in that body. Using the following equation, the air temperature can be obtained from the values of LST: Equation (1) In order to prove the accuracy of the work for the preparation of the surface temperature map, the temperature values measured by the three synoptic stations (Kordb ku-Blok, Gorgan and Nomal-Dam Kowsar) were compared on the same date with the obtained values of air temperature from the surface temperature values. Results and Discussion The results showed that bare land class has a higher temperature (45. 96 ° C) due to lack of protective cover. Since the vegetation is very limited and dispersed in the bare land areas, the Earth is more exposed to solar waves. On the other hand, the surface of the bare solid ground is bright, which affects energy absorption and increases surface temperature. While the use of irrigated agriculture and water resources was 29. 95 and 34. 33 degrees Celsius, the lowest average temperature was observed among other classes. Considering the time taken to get the image of products cultivated in agriculture, they had an acceptable level of growth and greenness (high NDVI index highlighted the greenery of arable crops on this date) and by influencing evaporation reduction and maintaining soil moisture in effective thermal modification Which have led to less solar heat absorption and eventually reduced temperature. Water resources also reduce the surrounding air due to its high heat capacity and low solar energy absorption. Since forest class is at higher altitudes, its surface temperature was studied separately. The comparison of the surface temperature of the pixels related to the use of forest and the forestry sector showed that the LST in the forestry sector was about 5 degrees Celsius above the forest class. According to the results, the correlation between the NDVI index and the surface temperature is 0. 65. The negative correlation obtained between this index and the surface temperature indicates an inverse relationship between this index and the surface temperature, and it can be deduced that in areas with high vegetation density such as forest use, surface temperature is much lower than other uses, which suggests a type of relationship Usage with surface temperature. According to Sig, this correlation is significant at 95% confidence level. Evaluation of surface temperature map prepared with ground data The results of the correlation test between the surface temperature of Landsat 8 and the air temperature of the meteorological station as well as the correlation between the air temperature and the existing stations were both obtained at 0. 99, which confirmed the accuracy of equation (1) used to convert the LST data to the data Air temperature. The difference in LST between stations in the area indicates that stations are located in different environmental conditions due to environmental factors such as elevation, slope, direction, distance from the sea on LST. Conclusion In this study, to determine the relationship between land use and LST, the surface temperature map of the area was prepared and the surface temperature of the area between 14 and 51 degrees Celsius was estimated. Since the height parameter has an effective effect on temperature, the samples were selected from the height points of each land use. Thus, the effect of height factor on the results of the research was neutralized. In areas where vegetation is dense, such as forest, surface temperatures are far lower than other uses. Also, the irrigated agriculture class, which had a higher density than rangelands, showed lower temperatures. On the other hand, the bare lands had the highest surface temperature. Therefore, it can be concluded that vegetation is a major factor in surface temperature, especially in areas where this coating is denser. The effect is more obvious.

Reduction in vegetation cover and increasing land surface temperature are the most important consequences of drought which leads to land degradation. Therefore, the evaluation of drought effects on vegetation cover and its relationship with land surface temperature is very important. To that end, the objective of this study was to evaluate the relationship among vegetation cover, drought and land surface temperature in the north-west of Iran during 2001-2014. The annual (12 months) Standardized Precipitation Index (SPI) was calculated using monthly precipitation time series from 26 meteorological stations in the study area. Then, the interpolated maps of drought were produced using the Kriging method in the GIS environment. The Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) index were calculated from 2001-2014 using MODIS satellite images. Then, the Pearson correlation coefficient (R) was calculated to investigate the relationship among NDVI, LST and SPI. According to the results, the changes trend of mean NDVI was similar to drought trends over the years (2001-2014) and the NDVI values have experienced its greatest reduction in 2008 (NDVI=0. 087). The results also showed that LST values had a significant inverse relationship with SPI and NDVI indices (P<0. 05). So, Land Surface Temperature (LST) was the highest (LST=22. 3) where SPI and NDVI were the lowest (SPI=0. 04 and NDVI=0. 087) and there was the most severe drought in these conditions. Therefore, mean NDVI and LST could be suitable alternatives for climate indicators in the monitoring and evaluation of drought events in semi-arid areas.

Investigating the trend of long-term changes of vegetation using time series data and based on land use can provide useful information. Since the Time series of Satellite images are one of the best sources available in this field Their use in environmental studies is effective. Statistical tests are essential for analyzing time series data, and one valid test is the non-parametric Mann-Kendall (MK) test. In this study, changes in NDVI index were investigated using MODIS image data in the Hamon-Jazmorian basin in the southeast of Iran. For this purpose, 524 images 16-day NDVI index of MODIS, were selected for the period from 2000 to 2022, and the vegetation in this basin was carefully analyzed. Then, trend of the change of this index during this 23-year period was investigated using the MK test. In the following, the pattern of changes of this index in the region was determined and the areas with vegetation changes were determined. The results showed that the northwestern and central areas of the basin, where vegetation has a higher density, underwent the most changes, with vegetation decreasing and increasing. Comparing the NDVI index difference map between the beginning and end of the investigated years with the land use map showed widespread risk in the Hamoon-Jazmourian basin. The results of this researchcan be used to investigate the long-term vegetation changes in different areas of the Hamoon-Jazmourian basin and to adopt appropriate policies and planning for the use of vegetation.

Remote sensing data play an important role in environmental planning and monitoring. The current study aimed to investigate the land surface temperature (LST) and the effect of environmental factors on the LST, to identify the temporal-spatial patterns and determine the hot spots in the period of 2013 to 2019, using Landsat 8 images. The effect of spectral indices: Normalized Difference Build-up Index (NDBI), Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) on the surface temperature was investigated. Results indicated that the lowest average temperature has occurred in 2019 and the highest LST was in the 2017. The results of Moran's index correlation also showed that the most clustering pattern of LST, with the Moran value of 0. 85 was obtained in 2019, the highest correlation between LST and NDBI, with the R value of 0. 76 in the 2015, the highest correlation between LST and NDVI in the 2015 (R =-0. 56), and the highest correlation between LST and NDWI in 2013 (R =-0. 53). Rasht watershed in Guilan province is affected by human factors and land use changes. Therefore, it is recommended to increase the vegetation cover in urban areas, reduce the change of pasture to agricultural area, and reduce forest destruction.