This article summarized the spatiotemporal pattern of land use/land cover (LU/LC) and urban heat island (UHI) dynamics in the Metropolitan city of Tehran between 1988 and 2018. The study showed dynamics of each LU/LC class and their role in influencing the UHI. The impervious surface area expanded by 286.04 (48.27% of total land) and vegetated land was depleted by 42.06 km² (7.10% of total land) during the period of 1988–2018. The mean land surface temperature (LST) has enlarged by approximately 2–3 °C at the city center and 5–7 °C at the periphery between 1988 and 2018 based on the urban–rural gradient analysis. The lower mean LST was experienced by vegetation land (VL) and water body (WB) by approximately 4–5 °C and 5–7 °C, respectively, and the higher mean LST by open land (OL) by 7–11 °C than other LU/LC classes at all time-points during the time period, 1988–2018. The magnitude of mean LST was calculated based on the main LU/LC categories, where impervious land (IL) recorded the higher temperature difference compared to vegetation land (VL) and water bodies (WB). However, open land (OL) recorded the highest mean LST differences with all the other LU/LC categories. In addition to that, there was an overall negative correlation between LST and the normal difference vegetation index (NDVI). By contrast, there was an overall positive correlation between LST and the normal difference built-up index (NDBI). This article, executed through three decadal change analyses from 1988 to 2018 at 10-year intervals, has made a significant contribution to delineating the long records of change dynamics and could have a great influence on policy making to foster environmental sustainability.

In this study, changes in the spatial and temporal patterns of climate extreme indices were analyzed. Daily maximum and minimum air temperature, precipitation, and their association with climate change were used as the basis for tracking changes at 50 meteorological stations in Iran over the period 1975–2010. Sixteen indices of extreme temperature and 11 indices of extreme precipitation, which have been quality controlled and tested for homogeneity and missing data, are examined. Temperature extremes show a warming trend, with a large proportion of stations having statistically significant trends for all temperature indices. Over the last 15 years (1995–2010), the annual frequency of warm days and nights has increased by 12 and 14 days/decade, respectively. The number of cold days and nights has decreased by 4 and 3 days/decade, respectively. The annual mean maximum and minimum temperatures averaged across Iran both increased by 0.031 and 0.059 °C/decade. The probability of cold nights has gradually decreased from more than 20 % in 1975–1986 to less than 15 % in 1999–2010, whereas the mean frequency of warm days has increased abruptly between the first 12-year period (1975–1986) and the recent 12-year period (1999–2010) from 18 to 40 %, respectively. There are no systematic regional trends over the study period in total precipitation or in the frequency and duration of extreme precipitation events. Statistically significant trends in extreme precipitation events are observed at less than 15 % of all weather stations, with no spatially coherent pattern of change, whereas statistically significant changes in extreme temperature events have occurred at more than 85 % of all weather stations, forming strongly coherent spatial patterns.

Drought has severe impacts on human society and ecosystems. In this study, we used data acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) and Tropical Rainfall Measuring Mission (TRMM) sensors to examine the drought effects on vegetation in Afghanistan from 2001 to 2018. The MODIS data included the 16-day 250-m composites of the Normalized Difference Vegetation Index (NDVI) and the Vegetation Condition Index (VCI) with Land Surface Temperature (LST) images with 1 km resolution. The TRMM data were monthly rainfalls with 0.1-degree resolution. The relationship between drought and index-defined vegetation variation was examined by using time series, regression analysis, and anomaly calculation. The results showed that the vegetation coverage for the whole country, reaching the lowest levels of 6.2% and 5.5% were observed in drought years 2001 and 2008, respectively. However, there is a huge inter-regional variation in vegetation coverage in the study period with a significant rising trend in Helmand Watershed with R = 0.66 (p value = 0.05). Based on VCI for the same two years (2001 and 2008), 84% and 72% of the country were subject to drought conditions, respectively. Coherently, TRMM data confirm that 2001 and 2008 were the least rainfall years of 108 and 251 mm, respectively. On the other hand, years 2009 and 2010 were registered with the largest vegetation coverage of 16.3% mainly due to lower annual LST than average LST of 14 degrees and partially due to their slightly higher annual rainfalls of 378 and 425 mm, respectively, than the historical average of 327 mm. Based on the derived VCI, 28% and 21% of the study area experienced drought conditions in 2009 and 2010, respectively. It is also found that correlations are relatively high between NDVI and VCI (r = 0.77, p = 0.0002), but slightly lower between NDVI and precipitation (r = 0.51, p = 0.03). In addition, LST played a key role in influencing the value of NDVI. However, both LST and precipitation must be considered together in order to properly capture the correlation between drought and NDVI.

Rapid urban growth processes give rise to impervious surfaces and are regarded as the primary cause of urban flooding or waterlogging in urban areas. The high rate of urbanization has caused waterlogging and urban flooding in many parts of Dhaka city. Therefore, the study is undertaken to quantify the changes in land use/land cover (LULC) and urban runoff extent based on the Natural Resources Conservation Service (NRCS) Curve Number (CN) during 1978–2018. The five-decadal LULC has been analyzed using three-generation Landsat time-series data considering six different classes, namely agriculture, built-up, wetland, open land, green spaces, and water bodies for the years 1978, 1988, 1998, 2007, and 2018. Significant changes in LULC for the study area from 1978–2018 are observed as 13.1%, 4.8%, and 7.8% reduction in agricultural land, green spaces, and water bodies, respectively, and a 22.1% increase in the built-up area is estimated. Within Dhaka city, 14.6%, 16.0%, and 12.3% reduction in agricultural land, green spaces, and water bodies, respectively, and a radical increase of 41.9% in built-up area are reckoned. The decadal runoff assessment has been carried out using the NRCS-CN method, considering an extreme rainfall event of 341 mm/day (13 September 2004). The catchment area under very high runoff category is observed as 159.5 km² (1978) and 318.3 km² (2018), whereas, for Dhaka city, the setting is dynamic as the area under the very high runoff category has increased from 74.24 km² (24.44%) to 174.23 km² (57.36%) in years 1978 and 2018, respectively, and, mostly, the very high runoff potential areas correspond to the dense built-up surfaces.

Land surface temperature (LST) is a basic parameter in energy exchange between the land and the atmosphere, and is frequently used in many sciences such as climatology, hydrology, agriculture, ecology, etc. Time series of satellite LST data have usually deficient, missing, and unacceptable data caused by the presence of clouds in images, the presence of dust in the atmosphere, and sensor failure. In this study, the singular spectrum analysis (SSA) algorithm was used to resolve the problem of missing and outlier data caused by cloud cover. The region studied in the present research included an image frame of the Moderate Resolution Imaging Spectroradiometer (MODIS) with horizontal number 22 and vertical number 05 (h22v05). This image involved a large part of Iran, Turkmenistan, and the Caspian Sea. In this study, MODIS LST products (MOD11A1) were used during 2015 with approximately 1 km × 1 km spatial resolution and day/night LST data (daily temporal resolution). On average, the data have 36.37% gaps in each pixel profile with 730 day/night LST data. The results of the SSA algorithm in the reconstruction of LST images indicated a root mean square error (RMSE) of 2.95 Kelvin (K) between the original and reconstructed LST time series data in the study region. In general, the findings showed that the SSA algorithm using spatio-temporal interpolation can be effectively used to resolve the problem of missing data caused by cloud cover.