Temporal dynamics of land use/land cover change and its prediction using CA-ANN model for southwestern coastal Bangladesh

Abstract

Change analysis of land use and land cover (LULC) is a technique to study the environmental degradation and to control the unplanned development. Analysis of the past changing trend of LULC along with modeling future LULC provides a combined opportunity to evaluate and guide the present and future land use policy. The southwest coastal region of Bangladesh, especially Assasuni Upazila of Satkhira District, is the most vulnerable to natural disasters and has faced notable changes in its LULC due to the combined effects of natural and anthropogenic causes. The objectives of this study are to illustrate the temporal dynamics of LULC change in Assasuni Upazila over the last 27 years (i.e., between 1989 and 2015) and also to predict future land use change using CA-ANN (cellular automata and artificial neural network) model for the year 2028. Temporal dynamics of LULC change was analyzed, employing supervised classification of multi-temporal Landsat images. Then, prediction of future LULC was carried out by CA-ANN model using MOLUSCE plugin of QGIS. The analysis of LULC change revealed that the LULC of Assasuni had changed notably during 1989 to 2015. "Bare lands" decreased by 21% being occupied by other land uses, especially by "shrimp farms." Shrimp farm area increased by 25.9% during this period, indicating a major occupational transformation from agriculture to shrimp aquaculture in the study area during the period under study. Reduction in "settlement" area revealed the trend of migration from the Upazila. The predicted LULC for the year 2028 showed that reduction in bare land area would continue and 1595.97 ha bare land would transform into shrimp farm during 2015 to 2028. Also, the impacts of the changing LULC on the livelihood of local people and migration status of the Upazila were analyzed from the data collected through focus group discussions and questionnaire surveys. The analysis revealed that the changing LULC and the occupational shift from paddy cultivation to shrimp farming were related to each other. Around 31.3% of the total respondents stated that at least one of their family members had migrated. Climate-driven southwestern coastal people usually migrate from the vulnerable rural areas towards the nearest relatively safe city due to adverse effects of natural disasters. To control the unplanned development and reduce the internal migration in Assasuni and other coastal areas, a comprehensive land use management plan was suggested that would accommodate the diversified uses of coastal lands and eventually lessen the threats to the life and livelihood of the local people.

Spatio-temporal Patterns of Land Use/Land Cover Change in the Heterogeneous Coastal Region of Bangladesh between 1990 and 2017

“…Due to the fact that these are unsupervised techniques, K-Means and ISODATA are often undesirable because they do not allow expert knowledge input (e.g., labeling of clusters) into the LULC map generation process. To tackle these challenges, previous research has adopted advanced classification algorithms including artificial neural networks (ANN), Support Vector Machines (SVM), Random Forest (RF), and Classification and Regression Trees (CART) [11,12]. RF and SVM algorithms are the most popular in the RS community [13,14] and are widely used for multispectral and hyperspectral image classification tasks.…”

“…A few studies exist that shed light on LULC change in the coastal areas of Bangladesh. While these studies highlight the space-time dynamic of coastal land-use, they are limited in geographic scope-i.e., focused on a relatively small coastal area comprising a few districts [4,12,36] or specific agro-ecological regions [3]. As such, the existing literature does not provide a detailed, comprehensive understanding on the changing pattern of LULC across the entire coastal region.…”

Past, Present and Future: Combining habitat suitability and future landcover simulation for long-term conservation management of Indian rhino

“…Neural Network (ANN) and Logistic Regression (LR) methods to model land cover transition potential. The LR is being used to predict the probability of occurrence of a particular event by the values of a set of features whereas the ANN uses backpropagation gradient calculation method which updates the weights of a multilayer perceptron 69,70 . The MOLUSCE plugin of QGIS was used to predict future land cover following the cellular automata (CA) model.…”

“…As MOLUSCE only work with raster data, all vector data sets were converted into raster, resampled at 30 × 30 m cell and were projected at WGS_1984_UTM_ZONE_ (45 N). For projecting the simulated results, the cellular-automata simulation was used, based on the Monte Carlo algorithm 69,[71][72][73][74] . The simulated map for the year 2028 was based on classified images of 2008 and 2018.…”

Dinâmica de uso e cobertura da terra e análise de tendência de mudanças para o município de Caçador, SC.

“…Esse tipo de análise foi projetada para extrair uma componente regional de um mapa, como a localização de uma tendência de mudança de uso e cobertura da terra de classes específicas (VÁCLAVÍK; ROGAN, 2009). As projeções de Markov baseiam-se no pressuposto de que as taxas de mudança observadas durante o período de calibração permanecerão as mesmas durante o período de simulação Rahman et al (2017).…”