3.3. Comparative Assessment of RF and XGBoost- Feature Selected RF in LULC Classification
As noted above (see Section 2.4
), to evaluate the performance of the XGBoost-RF classifier, a standalone RF classification was performed that yielded an average ~0.78 accuracy rate. Table 5
suggests this standalone RF classifier was outperformed by the XGBoost-feature selected RF classifier when classifying the LULC classes for every year. Table 7
shows (see also Figure A1
and Figure 4) the area of multi-temporal LULC classes obtained by RF and XGBoost-feature selected RF classifiers. LULC classifications performed by these two approaches differ quite significantly, particularly for agriculture, vegetation, and built-up area classes. Previous studies [3
] focused on estimating LULC classes of small discrete regions in the coastal areas of Bangladesh, for which it is difficult to empirically validate these results. The RF-based estimations of multi-year LULC areas do not appear to reflect growing economic activity and documented large-scale natural disturbances (e.g., cyclones, storm surges) in Bangladesh’s coastal region over recent decades.
For instance, RF-based classification underestimated the obvious impacts of the natural processes and urbanization on coastal LULC distributions, particularly for agricultural land, bare soil, and built-up areas. The percentage area, estimated solely via RF-based classifications, often yielded values that were close to zero for the aforementioned classes. An example is the year 2010, where the RF-based classification in Table 7
shows a built-up area of only 0.04%, a percentage which contradicts the findings of several studies [3
] that reported high rates of urbanization (hence an increased rate of development for built-up areas) in the past decade. Furthermore, the percentage area covered by the bare soil class was zero for the years 2005, 2010 and 2017, which for a hydrologically active area such as the coastal region of Bangladesh is quite impossible, especially because erosion and accretion processes (driven by the large river system) are highly prominent in this area [5
]. In contrast, XGBoost-RF-derived results are more aligned with the growing human and natural activity trend across the whole coastal region. Comparison of RF only and the XGBoost-RF values for the percentage area covered by the built-up class indicates that the latter is consistent with the findings of previous studies. The XGBoost-RF-derived percentages agree with the increasing trend of the built-up class from 2005–2017, as could be expected due to a proliferation in the amount of urbanization during the last decade. Similarly, contrasting the percentage area covered by the bare soil class during 2005–2017, it is evident that the XGBoost-driven classification technique has performed better than the solely RF-driven classification process. For example, the percentage areas for bare class in the XGBoost-driven classified images were 7.90, 0.38 and 0.52 for the years 2005, 2010 and 2017 respectively; compared to a percentage of 0 in the RF only classified images. In addition, declining coastal vegetation coverage alongside increasing agricultural land areas, increasing amounts of bare soil areas following natural disturbances (e.g., floods in 1998 and 2004; and cyclones of 2007 and 2009), and the increasing amount of built-up area were picked up comparatively well by the XGBoost-RF classification approach.
As mentioned previously, several studies have reported that RF’s randomized bagging-based feature selection and classification approach can be biased towards the majority class and therefore, can misclassify the minority class in the data [18
]. The findings from our study reiterate these shortcomings of the RF classifier, most notably when detecting minority classes (such as built-up land and bare soil) in a highly heterogeneous geomorphic setting.
3.4. Spatio-Temporal Patterns of LULC
Determining the spatio-temporal dynamics of LULC patterns in the coastal regions of Bangladesh requires a careful interpretation of the underlying natural and anthropogenic drivers that influence the pattern of LULC change. The coastal zone of Bangladesh is divided into western, eastern and central regions [64
]; and each of these zones has its own characteristics and geomorphic processes that shape the LULC pattern. Because of the interplay between these natural processes and human activities, people living in these areas are constantly subjected to severe flooding, cyclones, and storm surges, which cause extensive destruction of property and force displacements within and outside the region. For example, the southwestern region relies predominantly on the upstream waters for land building activity and experiences a myriad of environmental issues such as high-level soil and river water salinity [65
]. Similarly, the central zone is highly dynamic and various natural hazards such as storm surge-induced coastal flooding contribute to the environmental externalities. Although less frequent than the other two regions, the eastern zone is also affected by occasional cyclones and storm surges [3
] and has undergone huge infrastructural development in the recent years. These natural processes, combined with the recent increase in anthropogenic activities such as construction of embankments to secure people and properties from coastal flooding, infrastructural development and the advancement of tourism sector are greatly influencing coastal dynamics [5
]. Given the heavily intertwined human and natural processes within, for such a large and heterogeneous landscape, LULC classification using moderate resolution Landsat data is obviously a challenging task. As a result, and despite the use of powerful machine learning ensembles, some confusion in the detection and accurate classification of land covers is not surprising, especially when 10 out of the 19 coastal districts within the study area are susceptible to moderate to severe coastal flooding and cyclones [68
The LULC transition matrix (Table 8
), LULC maps (Figure 3
), and a per-year distribution of LULC class sizes (in hectare) (Figure 4
) demonstrate some interesting and important spatio-temporal patterns. It can be seen that the built-up areas experienced an increasing trend from 1990 to 2017, with a slight decrease in 2000 when a large proportion of human settlements on the southwestern coast was severely affected by the catastrophic flood of 2000 [72
]. The effect of this flood was probably manifested in the degradation of vegetation for 2000, since a large proportion of coastal vegetation cover was probably washed away. It appears, however, that successful coastal afforestation initiatives [73
] in successive years resulted in a gradual increase in vegetation, notably from 2000 to 2017 [73
]. Flooding has also influenced the distribution of inland water bodies. The devastating flood event of 2004 caused massive waterlogging in the low-lying coastal areas due to drainage congestion and the associated backwater effect, resulting in a sharp increase in water bodies in the classified LULC map of 2005. The residual effect of this event was probably linked with the increased proportion of the wetland category in 2005. Although filling of (grabbing) water bodies including river areas is a common practice in a region where rapid urbanization is taking place over the past decades, an increasing pattern of river area since 2005 was observed in the coastal region. This phenomenon can be associated with the increased popularity and practice of shrimp cultivation, with possible links to factors such as sea-level rise and land subsidence, especially in the southwestern region [76
]. Areas under agricultural activity showed an increasing trend between 1990 and 2000 with a decreasing trend in vegetation cover evident during the same period (Table 8
A detailed analysis of the intra and inter-transition of LULC classes (Table 8
) reveals that human-environment interactions are shaping the LULC dynamics of the coastal region. For example, contrary to common knowledge and perception, built-up areas in the coastal region of Bangladesh experienced conversion into other land cover types (e.g., agricultural land). This somewhat unconventional land use transition may be linked with coastal out-migration, induced predominantly by environmental disturbances and climate change [79
]. Although it is not entirely clear as to how human migration could lead to land cover change, Braimoh (2004) demonstrated that seasonal migration resulted in dramatic land use change in Ghana [81
]. Fasona and Omojola (2009), using remote sensing data, also noted that increased soil salinity via coastal erosion caused the loss of livelihoods, eventually leading to the dislocation of a total of 39 coastal communities in Nigeria [82
]. In the case of the coastal area of Bangladesh, it is possible that migration may force people to sell their properties to local elites, and it is, in fact, the choice of the buyers as to whether to keep the newly purchased land in its present form or to convert it to other categories such as shrimp land for a higher economic return. However, this warrants further investigation.
In order to explore the transition of built-up class in detail, we examined the last two population censuses (2001 and 2011) of the coastal districts, and found that four districts located in the central and southwestern zones experienced negative population changes between 2001 and 2011 (Table 9
). A comparison of these population changes against yearly LULC maps in Figure 3
also supports the observation that the areas experiencing a disappearance of built-up pixels (when compared to the previous years) belong to these four districts, experiencing a decrease in population. This strongly suggests that depopulation resulting from natural hazards or anthropogenic causes may play a significant role in explaining the decline in built-up areas in 2000 and the low intra-class transition of pixels in Table 7
and Table 8
, respectively. Furthermore, coastal erosion which is pervasive in many areas (particularly in the central zone), has, in the past, resulted in the loss of entire villages to the river or the sea. Given these observations, and the inconsistency in the built-up area/pixel transformation detailed, it is possible to suggest that the observations are indicative of the high-degree of human-environment interactions that govern LULC dynamics in Bangladesh’s coastal region.
However, it can also be observed that the conversion of built-up areas to other classes significantly declined after 2000 and now aligns more closely with the characteristics usually expected from this LULC class (remaining unchanged or showing an increased intra-class conversion). This could be the result of the various adaptation and disaster risk mitigation strategies implemented by the government and the non-government organizations in more recent times [69
The spatio-temporal patterns of agricultural land cover show a gradual increase between 1990 and 2000; however, this trend has fluctuated in the period between 2000 and 2017. One particular aspect that may govern this fluctuation in post 2000 years could result from increasing shrimp farming activities. The spate of recurrent cyclones and flooding has impacted on the stability of crop production as a livelihood practice. Additionally, increasing soil salinity also made crop production increasingly difficult for the farmers [39
]. Therefore, shrimp production, which is well suited to operating in the saline conditions and is less prone to environmental externalities, gained considerable prominence. The rising popularity of shrimp cultivation coupled with the natural calamities that took place during the study period (i.e., 1990–2017) can help understand the transition of agriculture to other classes in Table 8
. Periodic waterlogging caused by embankment construction is another factor that could have shaped agricultural land cover. Embankments that were constructed in the 1960s to secure people and properties in the coastal region is a prime example of man-made activity that appears to interrupt the sedimentation process, hence the elevation loss of the land [86
]. Consequently, water ingress into the areas behind the embankment walls during a cyclone or high tide events can lead to pervasive flooding of agricultural lands and human settlements [86
]. It is evident that the percent area covered by the agricultural land remained almost unchanged during the 1990–1995 and 1995–2000 periods. However, a sharp decline was observed during 2000–2005 period, after which the change again became nearly constant in post-2005 periods. The recurrent floods and cyclones rendered most of the agricultural lands productively useless. The repeated displacements of people due to these natural disasters may have led to the abandonment of these agricultural lands, which were later colonized by different forms of vegetation cover (such as shrubs and bushes) and thus, accounting for the high transition between these two classes. Additionally, a total change, ranging from 9.33 to 12.11%, was found for the conversion of agricultural class to water bodies and river classes during the 2000–2017 period. Both the conversion of agricultural lands to shrimp farming and the formation of large temporary water bodies due to floods and cyclones can potentially account for this change. In such a dynamic situation, the recognition of pure pixels (even with an advanced ensemble) could be challenging as satellite data only represent a snapshot of a given time point.
The river and wetland classes in LULC maps show an alternating trend of increase and decrease between 1990 and 2017. In the LULC classification process, isolating wetlands and river areas, was difficult for the years when coastal flooding occurred (either by high tide/cyclone induced storm surges), when river water was used for irrigation purpose or used for shrimp cultivation. These factors appear to have played a crucial role in the classification process by mixing up inland water bodies and river areas [87
]. Furthermore, after Cyclone Aila in 2009, numerous saline water bodies were created in the southwestern part of the region [89
]. When inland water bodies reach a certain salinity threshold, the spectral reflectance of these inland water bodies becomes identical to that of the rivers [88
]. Consequently, water bodies that are actually shrimp cultivation sites are classified as part of the river system. This aspect of spectral reflectance identification may be one of the reasons for the increasing trend of river areas being detected in the LULC maps of 2005, 2010 and 2017, as well as the high transition rate between water bodies and river classes in Table 8
. Additionally, one of the popularly-practiced adaptation strategies for flood and waterlogging in the southern region can be identified in the LULC transition matrix in Table 8
, that is—a large proportion of the water bodies seem to have converted into agricultural land. This may have resulted from alternating practice of shrimp cultivation and crop production in low-lying areas such as the beels
]. Farmers resort to shrimp cultivation when these low elevation lands become inundated with water after intense rainstorms, and thus, for those years, the low-lying areas were classified as water bodies. In succeeding years, when there is not sufficient influx of water from the rainy season or the tide from the nearby rivers, these areas dry up and are again used for agricultural activities. As a result, large areas of land could have varied spectral responses when they are engulfed with rain or saline waters, or are used for agricultural purposes. This type of issue may be a common source of mixed pixels while employing moderate resolution images for LULC classification.
A significant decline in the amount of vegetation cover can be seen in LULC maps between 1990 and 2000. An intricate web of river systems and estuaries along the central zone makes it highly dynamic and susceptible to high-tide induced coastal floods [5
], and this may result in the frequent submergence of built-up land and human settlements along with the removal of vegetation cover. Despite the eastern zone being relatively stable in terms of erosion and accretion processes [64
], clearing of forest cover is pervasive, together with other increased human activities such as infrastructural development and urban expansion [94
]. However, in the post 2000 years, areas with vegetation cover increased until 2017, which can be attributed to major coastal afforestation projects along the coastal belt undertaken since 2000 [73
]. As a part of these projects, the Department of Forest from the government of Bangladesh took a mega initiative of planting 2872.88 hectares of Nipa, 10.0 hectares of coconut, 40.0 hectares of Arica palm, 280.0 hectares of Bamboo and Cane, 192,395.24 hectares of mangrove and 8689.53 hectares of non-mangrove trees. The effects of these afforestation projects are clearly visible in the LULC maps between 2005 and 2017. Table 8
further shows that a large portion of the vegetation cover in the study area remained unchanged during the study period (1990–2017) and that the intra-class percent conversion remained nearly above 50% from 1995–2017. This could be due mainly to the large mangrove forest occupying the southwestern region of the study area, which did not show any discernible change in the LULC maps (Figure 3
a–f). Apart from this, vegetation plantations around homesteads are a popular measure used to save habitation and people from the ingress of frequent cyclones such as those that occurred in 2007 and 2009. This has become a common practice, to save communities against climate-influenced adversities, especially in the central coastal zone [95