An assessment of remote sensing-based drought index over different land cover types in southern Africa

Abstract

An understanding of drought and land cover interaction plays a crucial role in vegetation vulnerability studies and land use planning. However, there is paucity of information on drought, land cover and land use interaction in southern Africa. We analysed the drought impact on land cover using Globcover land cover data and Vegetation Condition Index (VCI) for the 2015 to 2016 season. The 2015 to 2016 season was chosen because it was the worst drought in southern Africa since the 1980s. We developed a novel land cover 'social pixels' or 'village pixels' which represents rural communities. The Kruskal-Wallis test was used to evaluate whether there is a significant difference in drought impact among the land cover classes. The response of each land cover to drought impact was calculated by correlating Standardized Precipitation Evapotranspiration Index (SPEI) and Normalized Difference Vegetation Index (NDVI). Our results reveal that the evergreen forests and the flooded vegetation were the most severely affected by the 2015-2016 drought. However, the lowest VCI values were recorded within the village pixels land cover, indicating the vulnerability of rural communities to drought impacts. The vegetation response to drought impact ranged from 2 months (crops) to 8 months (flooded vegetation). With regards to drought recurrence (1998 to 2018), the crop and grassland land cover recorded the highest drought frequency whilst the forest had the least drought frequency.

Time-Lag Effect of Climate Conditions on Vegetation Productivity in a Temperate Forest–Grassland Ecotone

“…The q values for the interaction effect between the vegetation type and soil texture factors on the best time lag of the NPP response to PPT and Ta were all greater than those for a single factor (Table 2), which indicated that the interaction effect between the vegetation type and soil texture factors was bivariable enhanced. In line with the findings of other researchers [16,55,56], there occurred a notable legacy effect of climate conditions, where vegetation growth was significantly affected by PPT, Ta, and SPEI in preceding months. Previous studies of ecotones determined that the ecotone migration response to climate warming was gradual, exhibiting a lag effect due to the resistance of retreating forest biomes [28].…”

“…Previous studies of ecotones determined that the ecotone migration response to climate warming was gradual, exhibiting a lag effect due to the resistance of retreating forest biomes [28]. This result is similar to ours in the considered forest-grassland ecotone, and details and a comparison are provided in Table 3 [16,55,[57][58][59][60]. In previous studies of the time-lag effect, the time lag ranged from 6.2 days to 9 months at the daily, monthly, and annual scales.…”

“…In addition to temporal dynamics, the time lag length of the NPP response to climate conditions varies in different vegetation types and soil textures at the spatial scale [12,16,17]. Compared to forest and desert biomes, grassland and agricultural biomes exhibit time-lag correlations with precipitation and temperature, respectively [7].…”

The Drought Regime in Southern Africa: A Systematic Review

“…El Niño is characterized by a positive and significant anomaly of SST in the equatorial Pacific Ocean near South America. The Southern Oscillation is an interannual fluctuation of atmospheric pressure at sea level over the Pacific Ocean, usually evaluated with the Southern Oscillation Index (SOI), which is a standardized index based on the sea level pressure (SLP) differences between Tahiti and Darwin, Australia [48,55,65].…”

Combining RUSLE model and the vegetation health index to unravel the relationship between soil erosion and droughts in southeastern Tunisia

“…Single or combined, these indices can assign droughts identification with respect to duration, severity, intensity, and location (Heim, 2002;Keyantash and Dracup, 2002). Although the standardized precipitation index (SPI), a precipitation-based index, is the most commonly used, remote sensing-based indices are increasingly important tools for droughts monitoring as they can surpass the limitations of other indices which often require multiple and serially complete data (Hazaymeh and Hassan, 2017;Marumbwa et al, 2020).…”