An Application of Machine-Learning Model for Analyzing the Impact of Land-Use Change on Surface Water Resources in Gauteng Province, South Africa

The change in land-use diversity is attributed to the anthropogenic factors sustaining life. The surface water bodies and other crucial natural resources in the study area are being depleted at an alarming rate. This study explored the implications of the changing land-use diversity on surface water resources by using a random forest (RF) classifier machine-learning algorithm and remote-sensing models in Gauteng Province, South Africa. Landsat datasets from 1993 to 2022 were used and processed in the Google Earth Engine (GEE) platform, using the RF classifier. The results indicate nine land-use diversity classes having increased and decreased tendencies, with high F-score values ranging from 72.3% to 100%. In GP, the spatial coverage of BL has shrunk by 100.4 km2 every year over the past three decades. Similarly, BuA exhibits an annual decreasing rate of 42.4 km2 due to the effect of dense vegetation coverage within the same land use type. Meanwhile, water bodies, marine quarries, arable lands, grasslands, shrublands, dense forests, and wetlands were expanded annually by 1.3, 2.3, 2.9, 5.6, 11.2, 29.6, and 89.5 km2, respectively. The surface water content level of the study area has been poor throughout the study years. The MNDWI and NDWI values have a stronger Pearson correlation at a radius of 5 km (r = 0.60, p = 0.000, n = 87,260) than at 10 and 15 km. This research is essential to improve current land-use planning and surface water management techniques to reduce the environmental impacts of land-use change

Analysis of the long-term agricultural drought onset, cessation, duration, frequency, severity and spatial extent using Vegetation Health Index (VHI) in Raya and its environs, Northern Ethiopia

Abstract Background Droughts cause serious effects on the agricultural and agro-pastoral sector due to its heavy dependence on rainfall. Several studies on agricultural drought monitoring have been conducted in Africa in general and Ethiopia in particular. However , these studies were carried out using the limited capacity of drought indices such as Normalized Difference Vegetation Index (NDVI), Vegetation Condition Index (VCI), and Deviation of Normalized Difference Vegetation Index (DevNDVI) only. To overcome this challenge, the present study aims to analyze the long-term agricultural drought onset, cessation, duration, frequency, severity and its spatial extents based on remote sensing data using the Vegetation Health Index (VHI) 3-month time-scale in Raya and its surrounding area, Northern Ethiopia. Both the MOD11A2 Terra Land Surface Temperature (LST) and eMODIS NDVI at 250 by 250 m spatial resolution and hybrid TAMSAT monthly rainfall data were used. A simple linear regression model was also applied to examine how the agricultural drought responds to the rainfall variability. Results Extremely low mean NDVI value ranged from 0.23 to 0.27 was observed in the lowland area than mid and highlands. NDVI coverage during the main rainy season decreased by 3–4% in all districts of the study area, while LST shows a significant increase by 0.52–1.08 °C. VHI and rainfall value was significantly decreased during the main rainy season. Agricultural drought responded positively to seasonal rainfall (R2 = 0.357 to R2 = 0.651) at p < 0.01 and p < 0.05 significance level. This relationship revealed that when rainfall increases, VHI also tends to increase. As a result, the event of agricultural drought diminished. Conclusions Remote sensing and GIS-based agricultural drought can be better monitored by VHI composed of LST, NDVI, VCI, and TCI drought indices. Agricultural drought occurs once in every 1.36–7.5 years during the main rainy season, but the frequency, duration and severity are higher (10–11 times) in the lowland area than the mid and highlands area (2–6 times) during the last 15 years. This study suggests that the effect of drought could be reduced through involving the smallholder farmers in a wide range of on and off-farm practices. This study may help to improve the existing agricultural drought monitoring systems carried out in Africa in general and Ethiopia in particular. It also supports the formulation and implementation of drought coping and mitigation measures in the study area

Analysis of drought coping strategies in northern Ethiopian highlands

Abstract One of the most detrimental concerns brought on by a changing climate that annually affects many people's lives is drought. Proactive and reactive drought coping and adapting mechanisms enable farmers to be resilient against climate–induced drought and improve the drylands' current disaster preparedness and early warning systems. The aim of this study was to assess proactive and reactive farmers' drought coping strategies at household level in Raya Valley in southern Tigray, Ethiopia. Agro–climatological based 246 households were sampled from the lowlands, midlands and highlands of the study area. The most effective drought coping mechanisms were discovered using a multinomial logit model. The study area had endured mild to extremely severe drought in the last three decades. The association between the various drought severity and household heads were significant (chi2 = 9.861, df = 3, p < 0.05). Proactive drought coping measures included collecting and storing pasture, conserving soil and water, weather prediction information to adjust saving and farming practices. Livestock feeding with roasted cactus cladode, small business loans, livestock selling, productive safety–net program, and food consumption reduction were the major reactive drought coping strategies. The proactive and reactive drought coping strategies identified in this study should be used to improve the existing disaster preparedness and early warning systems in the face of climate and weather extreme related impacts of climate variability and change