Abstract
Land use and land cover (LULC) change have significant consequences on
habitat and environment. Scholars have developed several LULC models to
identify the factors behind the changes and to simulate future LULC
scenarios to assist in policymaking. Nevertheless, the accuracy of the
models remains contentious and a matter of ongoing research agenda.
Additionally, most of these studies used a training dataset to train the
model and a validation dataset, which is a part of the original training
dataset used to validate the model’s accuracy. However, to justify
model’s actual predictive capability, we need to test the model on
real-world dataset that was not used in modeling. So, we present XGBoost
model to improve the accuracy of LULC prediction. Contrary to the
typical studies, we use a separate test dataset to justify the model’s
predictive capacity in real-world scenario. The result reveals that
XGBoost model exhibits highest 84% kappa and 93% accuracy score
compared to two benchmark model LR-CA (82% kappa and 92% accuracy
score) and ANN-CA (82% kappa and 92% accuracy score). We also found
that the built-up area increased by 48.7% in 2002 to 64% in 2010,
while agricultural and vacant land declined by almost at the same
magnitude over the period and the most important aspect of the LULC
shift process in Khulna city was the proximity factors to major roads,
industry and commercial establishments. The proposed model proved to
increase the predictive accuracy making it much more reliable for
analyzing and predicting urban LULC using spatial factors.