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Showing 3 results for Trend Analysis

M. Araghi Shahri, S. Soltani, M. Tarkesh, S. Pourmanafi,
Volume 9, Issue 3 (11-2020)
Abstract

One of the main scientific topics on the effects of global climate change is to assess changes in the carbon cycle in rangelands. Net Primary Production (NPP) is an important component of this cycle, in terms of carbon storage, and a key indicator for assessing the ecosystem function. This research aimed to investigate the correlation between NPP and ocean-atmospheric oscillations, monthly and seasonally, from 2000 to 2016 in the north of Iran’s Alborz Mountains. Net Primary Production of terrestrial vegetation was extracted from MODIS data and used in a model along with ocean-atmospheric oscillations. Multivariate regression analysis was used to investigate the simultaneous and lagged status in different timescales. Mann-Kendal test was used for trend analysis in different seasons over the studied period. Results showed that the highest NPP values were 2.06 and 1.30 g C m-2 d-1 in spring and summer and the lowest were 0.68 and 0.55 g C m-2 d-1 in autumn and winter, respectively. The trend of NPP variations was significantly different in autumn and winter. Overall, it was showed that NPP was affected by climatic variables, especially precipitation, and variables  related to ENSO indicator are the main factors affecting precipitation, thereby affecting NPP in the north of Iran.

Z. Sanaee, R. Modares, P. Mohit Esfahani,
Volume 12, Issue 3 (12-2023)
Abstract

The present study was conducted to investigate NPP changes and its relationship with drought. The monthly time series of NPP for the country's rangelands was prepared from the sum of net photosynthesis production (PSN) of the eight-day MODIS sensor (MOD17A2H) with a spatial scale of 500 m for 2000 to 2022.The monthly rainfall data of 165 synoptic stations were obtained from the National Meteorological Organization to calculate the Standard Precipitation Index (SPI) and monitor droughts in this period. Box plots of precipitation and NPP were prepared and showed that the highest values of NPP with an average of more than 50 gC.m-2month-1 occur during spring and early summer in humid areas. Results showed that NPP is increasing in Iran's rangeland ecosystems, and on average, the NPP of all rangeland ecosystems in the country is about 112.6 ± 13 gC.m-2.year-1  ,in other words, about 76 million tons of carbon per year. It is absorbed by the plants in the rangeland ecosystems of the country. The trend of changes in the monthly series of NPP was not significant due to the influence of seasonal characteristics, but the annual changes of NPP showed a significant trend of increase in all regions.

A. Bazrmanesh, S. Soltani Koupaei, M. Tarkesh Esfahani, R. Jafari,
Volume 12, Issue 4 (2-2024)
Abstract

The present study aimed to investigate  the spatio - temporal changes in  Leaf Area Index (LAI) and Land Surface Temperature (LST). This research examined the spatial and temporal trends of LST and LAI across the climatic zones of northern Iran from 2001 to 2022, analyzing the data both seasonally and annually using the remote sensing data from MODIS. To assess the trends in the indices studied, the prewhitened Mann-Kendall test was employed due to the significant autocorrelation present in the time series. The trend slope was calculated using  Sen's slope estimator. The results showed that during the study period, the leaf area index (LAI) increased across all bioclimatic zones. In forest ecosystems, a direct relationship between LAI and LST was observed in all seasons. In contrast,  rangeland ecosystems exhibited a  direct relationship in spring and summer, but an inverse relationship in autumn and winter. The Z-values were 2.825 for forests in humid temperate areas and 2.944 for rangelands in semi-arid, windy regions, both indicating a significant greening trend at a 99% confidence level. Analyzing the relationship between changes in LAI and LST can help develop effective management strategies to enhance ecosystem quality in northern regions and adapt to climate change.


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