Spatial statistics techniques for SPEI and NDVI drought indices: a case study of Khuzestan Province

Abstract

Drought is a major water resources management issue in Iran. Khuzestan Province is in a drought state due to water shortage. Therefore, identifying areas at high risk of drought and when drought occurs is essential for drought management. For this purpose, this study used precipitation and temperature data of 12 selected stations and MODIS sensor images from the United States Geological Survey database in 2000–2017. The Standardized Precipitation Evapotranspiration Index (SPEI) and the Standardized Normalized Difference Vegetation Index (NDVI) were calculated using the Hargreaves–Samani method and ENVI software. Moreover, different spatial statistics techniques were used in the ArcGIS environment to analyze the results. Also, time series diagrams were drawn, and the trend was evaluated using the Mann–Kendall test. Finally, the distribution of NDVI values was investigated using EasyFit software, and the amount of drought damage was determined using NDVI. The investigation of the cluster maps of the Anselin Local Moran’s Index along with hot and cold spots formed for both SPEI and NDVI showed that drought severity was higher at the southern stations than at the semi-northern and northwestern ones in the province. Moreover, the survey results using the EasyFit software showed that the southern stations, including the Ahvaz, Mahshahr, and Omidiyeh-Aghajari stations, were more at risk of drought than the other stations due to the drought threshold. Furthermore, the total damage caused by drought for the Ahvaz and Abadan stations showed a damage rate of 50%.

From pixels to riverscapes: How remote sensing and geospatial tools can prioritize riverscape restoration at multiple scales

“…Streams need space. Healthy streams are dynamic, regularly shifting their position within their valley bottom, reworking, and reconnecting to their floodplain (i.e., lateral connectivity; Biron et al, 2014;Kondolf, 2011;Naiman et al, 2010). 2.…”

“…To assess riverscape resilience, in this case, the ability of a riverscape to be unaffected by drought, we investigated the relationship between vegetation vigor (NDVI) and the standardized precipitation evapotranspiration index (SPEI), which considers both precipitation and potential evapotranspiration and is standardized to provide a normal distribution over the period of record, where more negative values represent more dryness (Beguería et al, 2010). SPEI has been well‐vetted in research related to drought and climate change (e.g., Byakatonda et al, 2018; Nejadrekabi et al, 2022; Páscoa et al, 2017; Yao et al, 2018), and the effect of drought on ecosystems (e.g., Barbeta et al, 2013; Cavin et al, 2013; Drew et al, 2013; Martin‐Benito et al, 2013; Stefandis et al, 2023; Vicente‐Serrano et al, 2013; Wei et al, 2022). We acquired SPEI data from the GRIDMET climate data collection (Abatzoglou, 2013) at the 9‐month timescale.…”

The normalized difference vegetation index (NDVI) of the Zat valley, Marrakech: comparison and dynamics

“…The spatio-temporal dynamics of vegetation cover is largely driven by climate factors, such as climate change and human activities, such as deforestation/reforestation, and topographic parameters such as elevation and slope, according to many studies that used the Normalized Difference vegetation index (NDVI) as a remote sensing tool, for monitoring vegetation dynamics ( Adole et al., 2016 ; Kogo et al., 2019 ; Chu et al., 2019 ; Lin et al., 2020 ; Matas-Granados et al., 2022 ; Prăvălie et al., 2022 ; Zhang et al., 2018 ; Duarte et al., 2018 ; Huang et al., 2021 ). These researchers and many others ( Höpfner and Scherer, 2011 ; Kalisa et al., 2019 ; Nse et al., 2020 ; Nejadrekabi et al., 2022 ) have studied the response of vegetation coverage to climate factors at different spatial and temporal scales based on NDVI time series, and the results show a high dependence of vegetation dynamics on precipitation ( Ezaidi et al., 2022 ), especially in arid and semi-arid regions ( Hou et al., 2013 ; Maimouni et al., 2021 ).…”

Applying Machine Learning for Threshold Selection in Drought Early Warning System

“…In this study, we examine the relationship between the NDVI and drought category. NDVI is related to the biomass and greenness of vegetation, which serves as an indicator to understand vegetation health at a both uniform and global scale [16,17]. Our problem can be formulated as follows: Given the NDVI data, and the Australian Gridded Climate Dataset (AGCD), return the optimal NDVI threshold values to represent the boundary of different drought categories, namely, extreme drought, severe drought, moderate drought, mild drought, and no drought.…”