Forest fires are key ecosystem modifiers affecting the biological, chemical, and physical attributes of forest soils. The extent of soil disturbance by fire is largely dependent on fire intensity, duration and recurrence, fuel load, and soil characteristics. The impact on soil properties is intricate, yielding different results based on these factors. This paper reviews research investigating the effects of wildfire and prescribed fire on the biological and physico-chemical attributes of forest soils and provides a summary of current knowledge associated with the benefits and disadvantages of such fires. Low-intensity fires with ash deposition on soil surfaces cause changes in soil chemistry, including increase in available nutrients and pH. High intensity fires are noted for the complete combustion of organic matter and result in severe negative impacts on forest soils. High intensity fires result in nutrient volatilization, the break down in soil aggregate stability, an increase soil bulk density, an increase in the hydrophobicity of soil particles leading to decreased water infiltration with increased erosion and destroy soil biota. High soil heating (> 120 °C) from high-intensity forest fires is detrimental to the soil ecosystem, especially its physical and biological properties. In this regard, the use of prescribed burning as a management tool to reduce the fuel load is highly recommended due to its low intensity and limited soil heating. Furthermore, the use of prescribed fires to manage fuel loads is critically needed in the light of current global warming as it will help prevent increased wildfire incidences. This review provides information on the impact of forest fires on soil properties, a key feature in the maintenance of healthy ecosystems. In addition, the review should prompt comprehensive soil and forest management regimes to limit soil disturbance and restore fire-disturbed soil ecosystems.

Infiltration is an important component of the hydrological cycle. It provides soil moisture in the vadose zone to support plant growth. This study was conducted to compare the validity of four infiltration models with measured values from the double ring infiltrometer. The parameters of the four models compared were estimated using the linear regression analysis. The C.C was used to show the performance of the predictability of the models. The RMSE, MAE and MBE were employed to check the anomalies between the predicted and the observed values. The results showed that, average values of the C.C ranged from 0.9294-0.9852. The average values of the RMSE were 4.0033, 17.489, 11.2400 and 49.8448; MAE were 3.1341, 15.9802, 10.6525, and 61.4736; and MBE were 0.0786, 9.5755, −0.0007 and 47.0204 for Philip, Horton, Green Ampt and Kostiakov respectively for the wetland soils. Statistical results also from the Fisher's multiple comparison test show that the mean infiltration rate estimated from the Green Ampt's, Philip's and Horton's model was not significantly different (p > 0.05) from the observed. The results indicated that the Kostiakov's model had the highest deviations as it overestimated the measured data in all the plots. Comparison of the statistical parameters C.C, RMSE, MAE, and MBE for the four models indicates that the Philip's model agreed well with the measured data and therefore, performed better than the Green Ampt's, Horton's and Kostiakov's models respectively in that order for Besease wetland soils. Estimation of infiltration rate by the Philip's model is important in the design of irrigation schemes and scheduling. Therefore, in the absence of measured infiltration data, the Philip's model could be used to produce infiltration information for inland valley bottom

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Forest loss, unbridled urbanisation, and the loss of arable lands have become contentious issues for the sustainable management of land. Landsat satellite images for 1986, 2003, 2013, and 2022, covering the Kumasi Metropolitan Assembly and its adjoining municipalities, were used to analyse the Land Use Land Cover (LULC) changes. The machine learning algorithm, Support Vector Machine (SVM), was used for the satellite image classification that led to the generation of the LULC maps. The Normalised Difference Vegetation Index (NDVI) and Normalised Difference Built-up Index (NDBI) were analysed to assess the correlations between the indices. The image overlays of the forest and urban extents and the calculation of the annual deforestation rates were evaluated. The study revealed decreasing trends in forestlands, increased urban/built-up areas (similar to the image overlays), and a decline in agricultural lands. However, there was a negative relationship between the NDVI and NDBI. The results corroborate the pressing need for the assessment of LULC utilising satellite sensors. This paper contributes to the existing outlines for evolving land design for the promotion of sustainable land use.