THE EXPLAINABILITY OF GRADIENT-BOOSTED DECISION TREES FOR DIGITAL ELEVATION MODEL (DEM) ERROR PREDICTION

Gradient boosted decision trees (GBDTs) have repeatedly outperformed several machine learning and deep learning algorithms in competitive data science. However, the explainability of GBDT predictions especially with earth observation data is still an open issue requiring more focus by researchers. In this study, we investigate the explainability of Bayesian-optimised GBDT algorithms for modelling and prediction of the vertical error in Copernicus GLO-30 digital elevation model (DEM). Three GBDT algorithms are investigated (extreme gradient boosting - XGBoost, light boosting machine – LightGBM, and categorical boosting – CatBoost), and SHapley Additive exPlanations (SHAP) are adopted for the explainability analysis. The assessment sites are selected from urban/industrial and mountainous landscapes in Cape Town, South Africa. Training datasets are comprised of eleven predictor variables which are known influencers of elevation error: elevation, slope, aspect, surface roughness, topographic position index, terrain ruggedness index, terrain surface texture, vector roughness measure, forest cover, bare ground cover, and urban footprints. The target variable (elevation error) was calculated with respect to accurate airborne LiDAR. After model training and testing, the GBDTs were applied for predicting the elevation error at model implementation sites. The SHAP plots showed varying levels of emphasis on the parameters depending on the land cover and terrain. For example, in the urban area, the influence of vector ruggedness measure surpassed that of first-order derivatives such as slope and aspect. Thus, it is recommended that machine learning modelling procedures and workflows incorporate model explainability to ensure robust interpretation and understanding of model predictions by both technical and non-technical users

Positional Accuracy Assessment of Historical Google Earth Imagery

Google Earth is the most popular virtual globe in use today. Given its popularity and usefulness, most users do not pay close attention to the positional accuracy of the imagery, and there is limited information on the subject. This study evaluates the horizontal accuracy of historical GE imagery at four epochs between year 2000 and 2018, and the vertical accuracy of its elevation data within Lagos State in Nigeria, West Africa. The horizontal accuracies of the images were evaluated by comparison with a very high resolution (VHR) digital orthophoto while the vertical accuracy was assessed by comparison with a network of 558 ground control points. The GE elevations were also compared to elevation data from two readily available 30m digital elevation models (DEMs), the Shuttle Radar Topography Mission (SRTM) v3.0 and the Advanced Land Observing Satellite World 3D (AW3D) DEM v2.1. The most recent GE imagery (year 2018) was the most accurate while year 2000 was the least accurate. This shows a continuous enhancement in the accuracy and reliability of satellite imagery data sources which form the source of Google Earth data. In terms of the vertical accuracy, GE elevation data had the highest RMSE of 6.213m followed by AW3D with an RMSE of 4.388m and SRTM with an RMSE of 3.682m. Although the vertical accuracy of SRTM and AW3D are superior, Google Earth still presents clear advantages in terms of its ease of use and contextual awareness.Comment: 36 page

Effect of Process Parameters on Efficiency of a Mechanical Expression Rig for Almond Oil and Its Physico-chemical Properties

The process parameters that can affect the extraction efficiency of a mechanical expression rig (MER) for almond kernel (Terminalia catappa) were investigated in an optimization study using central composite design (CCD). A four factor, five levels of CCD was applied to study the effect of moisture content (6 - 10% w.b), temperature (80 – 100oC), heating time (10 – 26 min.) and applied pressure (5.84 – 7.01MPa) using the chosen rage. The physico-chemical properties of the expressed Almond oil were also determined using standard procedure. The results of the study showed that all the variables significantly affected the expression efficiency at 95% confidence level. The optimum expression efficiency of 76.35% was obtained at temperature, pressure, heating time and moisture content of 93.34oC, 6.44MPa, 17.16 minutes and 8.71% wb respectively. This indicates that the MER performed satisfactorily. The experimental values were very close to the predicted values with p<0.05.The regression model obtained has provided a basis for selecting optimal process parameters for the recovery of oil from almond kernel using the MER. The physico-chemical properties investigated showed that refractive index, viscosity, saponification value, iodine value, free fatty acid value, acid value, peroxide value, flash, and fire points are within 2 the range suitable for many purposes as it showed that it is edible and conforms to CODEX standard for edible oil