Abstract
Although a detailed analysis of land use and land cover (LULC) change is essential in providing a greater understanding of increased human-environment interactions across the coastal region of Bangladesh, substantial challenges still exist for accurately classifying coastal LULC. This is due to the existence of high-level landscape heterogeneity and unavailability of good quality remotely sensed data. This study, the first of a kind, implemented a unique methodological approach to this challenge. Using freely available Landsat imagery, eXtreme Gradient Boosting (XGBoost)-based informative feature selection and Random Forest classification is used to elucidate spatio-temporal patterns of LULC across coastal areas over a 28-year period (1990–2017). We show that the XGBoost feature selection approach effectively addresses the issue of high landscape heterogeneity and spectral complexities in the image data, successfully augmenting the RF model performance (providing a mean user’s accuracy > 0.82). Multi-temporal LULC maps reveal that Bangladesh’s coastal areas experienced a net increase in agricultural land (5.44%), built-up (4.91%) and river (4.52%) areas over the past 28 years. While vegetation cover experienced a net decrease (8.26%), an increasing vegetation trend was observed in the years since 2000, primarily due to the Bangladesh government’s afforestation initiatives across the southern coastal belts. These findings provide a comprehensive picture of coastal LULC patterns, which will be useful for policy makers and resource managers to incorporate into coastal land use and environmental management practices. This work also provides useful methodological insights for future research to effectively address the spatial and spectral complexities of remotely sensed data used in classifying the LULC of a heterogeneous landscape.
Highlights
An accurate estimation of land use/land cover change (LULCC) is essential for improved understanding of its impacts on climatic and environmental systems, to enable the implementation of appropriate environmental management practices [1]
This study provided a detailed assessment of multi-temporal land use and land cover (LULC) changes in the coastal regions of Bangladesh using Landsat data and advanced feature selection and classification techniques—XGBoost and Random Forest
The XGBoost-based feature selection approach allowed detection of the most informative spectral bands. These contributed to the improved performance by the Random Forest (RF) classifier in producing six-class LULC maps. This joint XGBoost-RF approach performed substantially better than an independent RF classifier when dealing with high-level landscape heterogeneity and spectral complexity issues in 30-m Landsat images during the LULC classification process
Summary
An accurate estimation of land use/land cover change (LULCC) is essential for improved understanding of its impacts on climatic and environmental systems, to enable the implementation of appropriate environmental management practices [1]. The changing climate and its multifaceted, multi-scaled ramifications (e.g., sea-level rise, frequent storm surges) pose tremendous challenges to the coastal regions of Bangladesh, which include salinity intrusion, loss of vegetation, and loss of agricultural productivity. An estimated 35 million people currently live in the coastal belt of Bangladesh [2,3], with associated activities causing significant alteration to the coastal land cover features at various spatial and temporal scales [4]. These changes, for the most part driven by anthropogenic activities, contribute to the loss of ecosystem services to an increased risk of natural hazards. This, in turn, poses unique challenges for large-scale data processing and analyses
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