Mapping, intensities and future prediction of land use/land cover dynamics using google earth engine and CA- artificial neural network model

Abstract

Mapping of land use/ land cover (LULC) dynamics has gained significant attention in the past decades. This is due to the role played by LULC change in assessing climate, various ecosystem functions, natural resource activities and livelihoods in general. In Gedaref landscape of Eastern Sudan, there is limited or no knowledge of LULC structure and size, degree of change, transition, intensity and future outlook. Therefore, the aims of the current study were to (1) evaluate LULC changes in the Gedaref state, Sudan for the past thirty years (1988–2018) using Landsat imageries and the random forest classifier, (2) determine the underlying dynamics that caused the changes in the landscape structure using intensity analysis, and (3) predict future LULC outlook for the years 2028 and 2048 using cellular automata-artificial neural network (CA-ANN). The results exhibited drastic LULC dynamics driven mainly by cropland and settlement expansions, which increased by 13.92% and 319.61%, respectively, between 1988 and 2018. In contrast, forest and grassland declined by 56.47% and 56.23%, respectively. Moreover, the study shows that the gains in cropland coverage in Gedaref state over the studied period were at the expense of grassland and forest acreage, whereas the gains in settlements partially targeted cropland. Future LULC predictions showed a slight increase in cropland area from 89.59% to 90.43% and a considerable decrease in forest area (0.47% to 0.41%) between 2018 and 2048. Our findings provide reliable information on LULC patterns in Gedaref region that could be used for designing land use and environmental conservation frameworks for monitoring crop produce and grassland condition. In addition, the result could help in managing other natural resources and mitigating landscape fragmentation and degradation.

Analysis and Prediction of Land Use/Land Cover Changes in Korgalzhyn District, Kazakhstan

“…In this work, to predict changes in LULC by 2030 and 2050, the combined CA-ANN model was used, and studies were carried out using the MOLUSCE plugin of QGIS software, the effectiveness and reliability of which have been confirmed in many works [32][33][34][35][36][37]. In our case, the results of the validation of the forecast model also turned out to be relatively high (≥0.85), which gives grounds to consider the effects of modeling future changes in LULC quite reliable since 0.75 is regarded as an acceptable threshold [36].…”

“…Because CA-ANN is based on "what-if" scenarios, it is suitable for land change modeling studies [32]. It is believed that CA-ANN accurately represents the complex spatial inhomogeneities of LULC [33]; therefore, it is successfully used to evaluate future LULC transformations. At the same time, the Modules for Land-Use Change Simulation (MOLUSCE) plugin, as part of QGIS, is often used to model future LU generations based on CA-ANN [34][35][36][37][38][39].…”

Assessment of the environmental impacts of conflict-driven Internally Displaced Persons: A sentinel-2 satellite based analysis of land use/cover changes in the Kas locality, Darfur, Sudan

“…The NDVI was assessed and used to increase classification accuracy. The NDVI threshold for the separation of vegetation and the historical high-resolution images on Google Earth platform were used as a reference data [ 41 , 52 ] The onscreen digitization approach has been widely used and reported in previous studies for obtaining LULC classes, and it has been found to be reliable and accurate [ 53 , 54 ]. We generated 202 samples for the year 2016 and 216 samples for the year 2022, representing the five LULC classes.…”

Land-cover change in Cuba and implications for the area of distribution of a specialist’s host-plant

“…We selected Random Forest as the classification algorithm as it has been widely used on land use and land cover change investigations ( Alencar et al, 2020 ; Souza et al, 2020 ; Osman et al, 2023 ; Shimabukuro et al, 2023 ). Also, this algorithm is good at handling outliers and noisy datasets, has high processing speed ( Jin et al, 2018 ), has good performance with complex datasets ( Belgiu & Drăguţ, 2016 ), and has higher accuracy than other algorithms ( Sheykhmousa et al, 2020 ; Talukdar et al, 2020 ).…”