Showing 7 results for Vegetation
Zahra Hedjazizadeh, Sayyed Mohammad Hosseini, Ali Reza , Shokofe Layeghi,
Volume 0, Issue 0 (3-1921)
Abstract
Drought is a natural hazard that annually causes significant economic, social, environmental, and life-threatening damage in vast areas of the Earth. The damages caused by this phenomenon are intangible but very extensive and costly, which, if necessary, remote sensing techniques can be a useful tool in monitoring drought due to high temporal accuracy, wide spectral coverage, ease of access, no need for atmospheric correction and ground referencing. In recent years, the province of Hamedan has faced many problems due to frequent droughts. Therefore, the present study focused on investigating and monitoring drought in Hamedan province using the Temperature Condition index and its impact on the vegetation cover of the province using Advanced Very High Resolution Radiometer (AVHRR) and National Oceanic and Atmospheric Administration (NOAA) remote sensing data. First, the relevant data was extracted from the Nova star database, and finally, the spatiotemporal behavior of the vegetation cover drought index was examined on 1528 pixels in Hamedan province. The spatial resolution of the data used in this study is 4 kilometers. First, the relevant data were extracted from the Navstar database and ultimately, the spatiotemporal behavior of the drought index and vegetation cover was examined. The results indicate that drought has significantly increased the vegetation cover of Hamedan province based on remote sensing data. Kendall's coefficients indicate the presence of decreasing trends in vegetation cover at a 95 Percent confidence level. Only in May, June, and December has there been a slight decrease in vegetation cover within the extent of drought in the province. The spatial behavior analysis of the drought index on vegetation cover showed that February, March, as well as April have experienced more severe droughts within Hamedan province.
Saman Alimoradi, Asadollah : Khoorani, Yahya Esmaeilpoor,
Volume 17, Issue 44 (6-2017)
Abstract
The aim of this study is to retrieve land surface temperature (LST), air temperature (AT) and precipitation and to study their relationship with vegetation in rang lands of Karun watershed of Khuzestan province. For this purpose, land surface temperature (LST) and NDVI was drived from NOAA-AVHRR for maximum amount of greenness (April) for a period of 27 years. In order to extract LST, Price algorithm was used. Also air temperature and precipitation were interpolated for selected weather stations using IDW method. Spatial correlation outcomes (on 0.05) between NDVI with LST and air temperature show a reversed relation. This spatial relation is stronger for LST, so that this coefficient is often upper than 0.6, while seldom is 0.4 for air temperature and precipitation. Spatial regression models show that 62 percent of NDVI changes is determined by LST (R2=0.62) and air temperature and precipitation determine very limited amount of NDVI dynamics.
Mohammad Hossein Nasserzadeh, Zahra Hejazizadeh, Zahra Gholampour, Bohloul Alijani,
Volume 20, Issue 57 (6-2020)
Abstract
The plant community in an area is the most sensitive indicator of climate. A visual comparison of climate and vegetation on a global scale immediately reveals a strong correlation between climatic and vegetation zones and this relationship, of course, are not co-incidental. The main object of this study is to reveal the spatiotemporal association between climatic factors andvegetation Cover (NDVI) incorporate MODIS and TRMM product in Kohkiloyeh O Boirahmad province of Iran. So that the in this paer we use MOD13Q1 of MODIS product as NDVI layer for study area. MOD11A2 as landsurface temperature and 3B43 TRMM as meanmonthly accumulative rainfall for study area during 2002 to 2012 in 0.25° spatial resolution also were used as climatic factors. We use the correlation and cross-correlation analysis in 0.95 confident level(P_value =0.05) to detection the spatial and temporal association between the NDVI and 2 climatic Factor(LST and rainfall). The results indicated that during winter (December to March) the spatial distribution of NDVI is highly correlated with LST spatial distribution. In these months the pixels which have the high value of NDVI are spatiallyassociated with the pixels which have highest value of LST (6 to 14C°).As can be seen in table 1. Season the spatial correlation among NDVI and LST is so high which is statistical significant in 0.99 confident level in winter. In transient months such as May, October and November,(temperate months in study region ) the spatial correlation among NDVI and LST is falling to 0.30 to 0.35 which is not statistical significant in 0.95 confident level. Finally in summer season or warm months including Jun to September, we found the minimum spatial association among the NDVI and LST.. In temporal aspect we found that the maximum correlation between NDVI and LST simultaneously appears and not whit lag time. The spatial correlation of NDVI and TRMM monthly accumulative rainfall was statistical significant in spring season (April to Jun) by 1 month lag time in remain months we don’t find any significant correlation between NDVI and rainfall.
Saideh Eiyni, Dr Saeide Eini,
Volume 21, Issue 60 (3-2021)
Abstract
The aim of this research is to investigate drought stress in rangeland rangelands in Ardabil province. According to the monthly rainfall data, 4 synoptic stations of Ardebil province (Ardebil, Khalkhal, Meshgin Shahr and Parsabad Moghan) during the statistical period of 2016-1996 were used to calculate drought index (SEPI) index for 4 periods of 1, 3, 6 and 9 months. Landsat TM and OLI satellite imagery was also used to prepare landslide classification maps based on the maximum probability model and calculation of vegetation indices NDVI, EVI, SAVI and LAI. In order to investigate the relationship between the studied indices, Pearson correlation coefficient (R) and root mean square error (RMSE) have been used. The results of the classification showed that the extent of the rangelands of Ardebil province in 1394 in the year 1377, both in the rangeland and in the rangelands, is a significant decrease. According to the results of SPI, the drought condition during 2011-2015 is more than the other periods studied. Vegetation dispersal maps were based on decision tree tree classification algorithm and according to NDVI index for the studied months. Also, according to the results of the evaluation, the highest correlation was observed between the NDVI index and the 6-month SEPI index, and the lowest mean squared error was found between the SAVI index and the 6-month SEPI index, but in general, the most suitable indicator for Drought monitoring in Ardebil province pastures is a 6-month NDVI and SEPI indicator.
Hadi Zare Khormizie, Hamid Reza Ghafarian Malamiri,
Volume 23, Issue 69 (7-2023)
Abstract
Knowledge of rangeland vegetation characteristics as well as factors affecting it in environmental planning, land management and sustainable development is very important. However, regional and up-to-date maps of pasture vegetation cover are not always available. In this study, in order to plot the vegetation cover percentage of the rangelands and monitor its changes in drought and wet periods, NDVI products of MODIS sensor during the years from 2000 to 2017 with a spatial resolution of 250 m and a 16-day time resolution, and The SPI drought index were used. The study area is the part of the rangelands located in the Southern province of Yazd. In 2013, in order to provide ground truth data, a field work was done to take the sampling rate of vegetation from the rangeland level in the study area. According to the results, the NDVI index has a good ability to map vegetation cover, so the coefficient of determination (R2) between this index and the sample points was 0.71. Based on the results, the average vegetation cover of the studied area was 11.3% during the years 2000 to 2017. The highest and lowest amount of vegetation cover in the study area was in 2000 and 2002, with moderate mild conditions and very severe drought, respectively (14.6% and 9.2% respectively). The most important factors influencing the vegetation cover in the study area are rainfall and drought periods, so that the coefficient of determination (R2) between the SPI drought index and the average vegetation percentage was 0.85. In general, based on the results there is a high potential for assessing and monitoring rangeland vegetation changes using satellite data and remote sensing technique.
Akbar Mirahmadi, Hojjatollah Yazdan Panah, Mehdi Momeni,
Volume 24, Issue 72 (6-2024)
Abstract
In recent years, the technology of crop production has been greatly expanded using satellite data. Today, Landsat 8 and OLI sensor data, with a spatial resolution of 30 meters, allow the discovery of factors that control phenology on a local scale. In this study, the remote sensing indices - NDVI, EVI, Greenness, and Brightness - obtained from the OLI sensor and the GCC index obtained from digital camera images were used to estimate the phenological stages of the rapeseed plant. The Savitzky-Goli filter was used to remove outlier data and to produce smooth curves of time series of plant indices. The results showed that the curves obtained from the indices of NDVI, EVI, GCC show all four stages of remote sensing phenology – green-up, dormancy, maturity, and senescence - well, but the Greenness index did not show the dormancy stage well. The Brightness index curve shows the inverse behavior to other curves. According to Pearsonchr('39')s correlation test, GCC index data are correlated with NDVI and Brightness index data .we used the ratio threshold, rate of change and first derivative methods, to estimate "start of season" and "end of season" and the results showed that the first derivative and ratio threshold methods with an average difference of 18 and 19 days in the "start of the season" and the rate of change method, with an average difference of 8 days, has the best performance in estimating the “end of the season”. Also, the Brightness index with an average difference of 16 days and the EVI index with an average difference of 7 days have the best performance in estimating "start of season" and "end of season", respectively.
Ms Akram Alinia, Dr Amir Gandomkar, Dr Alireza Abasi,
Volume 24, Issue 75 (2-2025)
Abstract
The main goal of this research is to analyze the time series trend of fire events in natural areas and reveal the relationship between these fire events and vegetation levels in Lorestan province. In this regard, the data of the fire product of the Madis sensor (MOD14A1) and the vegetation product (MOD13A3) of the Madis sensor were used during the statistical period of 2000-2020. The monthly and annual spatial distribution of fires in Lorestan province was investigated. Cross-information matrix analysis and spatial correlation matrix were used to reveal the relationship between fire occurrences and vegetation. The results showed that more than 70% of the total frequency of fire occurrences in natural resources fields (fires with code 2) in Lorestan province is related to June and then July. In terms of the long-term trend, the 21-year trend of the frequency of fire incidents in the province showed that the frequency of incidents in the natural resources areas of the province has generally increased with an annual slope of 3 incidents. The results of the correlation analysis between the monthly vegetation cover and the annual frequency of fire occurrences showed that the fire occurrences in the province showed a significant correlation with the vegetation cover changes in 4 months of the growing period, i.e. from May to August. Cross-matrix analysis between the spatial distribution of fire occurrence foci and NDVI index, both of which were products of MODIS measurement, indicated that, in general, the highest frequency of fire occurrences in Lorestan province in the period from May to August corresponds to Greenness range was 0.15 to 0.22. This range of vegetation generally corresponded to rainfed lands, weak pastures and low-density forest patches
