Monitoring of land use land cover dynamics and prediction of urban growth using Land Change Modeler in Delhi and its environs, India

Past and future land use change dynamics: assessing the impact of urban development on agricultural land in the Pantura Jabar region, Indonesia

The conversion of large-scale agricultural land into urban areas poses a significant challenge to achieving national and global food security targets, as outlined in Sustainable Development Goal number 2, which aims to eradicate hunger. Indonesia has experienced a significant decline in rice field areas, with a reduction of approximately 650 thousand hectares within a year (2017-2018), the largest being in Java. Hence, this study aims to examine the impact of urban expansion on agricultural land in the north coast region of West Java Province from 2013 to 2020 and develop a predictive model for 2030 to support sustainable land use planning. The primary methods employed were random forest (RF) analysis using Google Earth Engine, intensity analysis, multilayer perceptron-neural network (MLP-NN), Markov chains-cellular automata (Markov-CA), and stakeholder interviews. The model also evaluated the influence of "distance to tollgates" as a previously unexplored driving factor in existing land use modeling studies. Landsat image classification results using the RF algorithm showed 87-88% accuracy. Cropland has historically been and is projected to remain the primary target for the expansion of built-up areas. Spatial planning irregularities were found in the growth of these areas that adversely affected farmers' socioeconomic and environmental conditions. Evaluation of land use models using MLP-NN and Markov-CA demonstrated an accuracy rate of 86.29-86.23%. The distance to tollgates factor significantly impacts the models, albeit less than population density. The 2030 intervention scenario, which implements a firm policy for sustainable agricultural land use, offers the potential to maintain the predicted cropland loss compared to business as usual.

Remote sensing-based seasonal surface urban heat island analysis in the mining and industrial environment

Cooling spaces have an optimistic influence on surface urban heat islands (SUHI). Blue spaces benefit from balancing the changing climate and heat variations. Because of the rapid deforestation and SUHI increase, the climate is gradually changing in Paschim Bardhhaman, West Bengal state, India. Paschim Bardhhaman has two sectors: specifically, Durgapur is the main industrial centre and Asansol has coal mines. This investigation aims to categorize spatiotemporal variations and seasonal differences in cooling spaces and their influence on SUHI, land use and land cover (LULC), and thermal differences using Landsat datasets for the years 1992, 2004, 2012, and 2022 in summer and winter. The coal mining and industrial range decreased from 10,391.92 (1992) to 3591.1 ha (2022), respectively. Open pit mining distresses fresh water by heavy water uses in ore processing, and mining water was applied to excerpt minerals. Among the two sub-divisions, the blue space amount was higher in Asansol because mining actions were higher in Asansol than in Durgapur. The open vegetation volume has reduced from 46,441.03 (1992) to 25,827.55 ha (2022) and dense vegetation has erased from 7368.02 (1992) to 15,608.56 ha (2022). Dense vegetation improved because of heavy precipitation in those regions. Mostly, Raghunathpur, Saraswatiganja, Bhagabanpur, Bistupur, Paschim Gangaram, Garkilla Kherobari, and Gourbazar have dense vegetation. The outcomes similarly demonstrate that the total built-up part has increased by 8412.82 ha in between 30 years. The built-up zone changes near the southeast and western Paschim Bardhhaman district. Those region needs appropriate attention and planning to survive soon.

Application of geospatial techniques and binary logistic regression model for analyzing driving factors of urban growth in Bahir Dar city, Ethiopia

Understanding the drivers of urban growth and spatiotemporal land use change is important for rational land use and sustainable urban development. Based on the land use data, GIS data of explanatory variables, experts' knowledge and field observation, the study used a binary logistic regression model (BLRM) to analyze factors that drive rapid urban growth in Bahir Dar city, Ethiopia, using the LOGISTICREG module in IDRISI Selva software. Nine factors were used to reflect the influence of proximity and physical factors on urban growth from 1984 to 2019. This model helped in quantifying and identifying the factors of urban growth, which includes topography (slope, elevation and aspect) and accessibility (Dis. to the main road, Dis. to international airport, Dis. to CBD, Dis. to existing built-up area, Dis. to forest land and Dis. to water body). Furthermore, urban growth probability maps were created based on LRM results, revealing that the biggest urban growth would occur around existing built-up areas along the main roads and near Bahir Dar international airport. The Relative Operating Characteristic (ROC) values of 0.85, 0.90 and 0.93 and PCP values of 96.72 %, 98.46 % and 98.51 % indicate the urban growth probability maps are valid and BLRM had an ideal ability to predict urban growth. So, the study highlighted the relation between urban growth and its drivers in Bahir Dar, giving a decision making framework for better land use management and resource allocation.

Spatio-temporal dynamics of land use transitions associated with human activities over Eurasian Steppe: Evidence from improved residual analysis

We presented a framework to evaluate the land use transformations over the Eurasian Steppe (EUS) driven by human activities from 2000 to 2020. Framework involves three main components: (1) evaluate the spatial-temporal dynamics of land use transitions by utilizing the land change modeler (LCM) and remote sensing data; (2) quantifying the individual contributions of climate change and human activities using improved residual trend analysis (IRTA) and pixel-based partial correlation coefficient (PCC); and (3) quantifying the contributions of land use transitions to Leaf Area Index Intensity (LAII) by using the linear regression. Research findings indicate an increase in cropland (+1.17 % = 104,217 km2) over EUS, while a - 0.80 % reduction over Uzbekistan and - 0.16 % over Tajikistan. From 2000 to 2020 a slight increase in grassland was observed over the EUS region by 0.05 %. The detailed findings confirm an increase (0.24 % = 21,248.62 km2) of grassland over the 1st half (2000-2010) and a decrease (-0.19 % = -16,490.50 km2) in the 2nd period (2011-2020), with a notable decline over Kazakhstan (-0.54 % = 13,690 km2), Tajikistan (-0.18 % = 1483 km2), and Volgograd (-0.79 % = 4346 km2). Area of surface water bodies has declined with an alarming rate over Kazakhstan (-0.40 % = 10,261 km2) and Uzbekistan (-2.22 % = 8943 km2). Additionally, dominant contributions of human activities to induced LULC transitions were observed over the Chinese region, Mongolia, Uzbekistan, and Volgograd regions, with approximately 87 %, 83 %, 92 %, and 47 %, respectively, causing effective transitions to 12,997 km2 of cropland, 24,645 km2 of grassland, 16,763 km2 of sparse vegetation in China, and 12,731.2 km2 to grassland and 15,356.1 km2 to sparse vegetation in Mongolia. Kazakhstan had mixed climate-human impact with human-driven transitions of 48,568 km2 of bare land to sparse vegetation, 27,741 km2 to grassland, and 49,789 km2 to cropland on the eastern sides. Southern regions near Uzbekistan had climatic dominancy, and 8472 km2 of water bodies turned into bare soil. LAII shows an increasing trend rate of 0.63 year-1, particularly over human-dominant regions. This study can guide knowledge of oscillations and reduce adverse impacts on ecosystems and their supply services.

Projection of bioclimatic patterns via CMIP6 in the Southeast Region of Türkiye: A guidance for adaptation strategies for climate policy

Over the past three decades, global urbanization and climate change have caused significant differences in climate conditions between urban and rural environments. The effects of global warming affect the climatic values in the urban area. The bioclimatic comfort in an area effectively chooses a site regarding the urban quality of life and activities. This study aims to predict the temporal and spatial changes of the bioclimatic comfort zones of Gaziantep province in terms of climate comfort in the context of long-term global scenarios. The future climate simulation maps were produced and analyzed comparing comfort conditions according to Shared Socioeconomic Pathways (SSPs) 245 and 585 scenarios of the Intergovernmental Panel on Climate Change's (IPCC) Coupled Model Intercomparison Project (CMIP) Phase 6 (CMIP6). Spatio-temporal changes in temperature, humidity, and bioclimatic comfort areas were analyzed to inform these efforts according to Thom's discomfort index (DI) and effective temperature-taking wind velocity (ETv). The current situation of bioclimatic comfort areas to examine their synergy under extreme hot weather throughout the province and their possible concerns in 2040, 2060, 2080, and 2100 were modeled using ArcGIS 10.8 software. SSP585/2100 will create hot (84%) areas, according to DI, and warm (29%) areas, according to ETv. The spatial results of the research are discussed, and some strategies are produced in terms of urban planning, design, and engineering.

Spatio-temporal change analysis and prediction of land use and land cover changes using CA-ANN model

The spatial and temporal representation of land use and land cover (LULC) changes helps to understand the interactions between natural habitats and other areas and to plan for sustainability. Research on the models used to determine the spatio-temporal change of LULC and simulation of possible future scenarios provides a perspective for future planning and development strategies. Landsat 5 TM for 1990, Landsat 7 ETM + for 2006, and Landsat 8 OLI for 2022 satellite imageries were used to estimate spatial and temporal variations of transition potentials and future LULC simulation. Independent variables (DEM, slope, and distances to roads and buildings) and the cellular automata-artificial neural network (CA-ANN) model integrated in the MOLUSCE plugin of QGIS were used. The CA-ANN model was used to predict the LULC maps for 2038 and 2054, and the results suggest that artificial surfaces will continue to increase. The Düzce City center's artificial surfaces grew by 100% between 1990 and 2022, from 16.04 to 33.10 km2, and are projected to be 41.13 km2 and 50.32 km2 in 2038 and 2054, respectively. Artificial surfaces, which covered 20% of the study area in 1990, are estimated to cover 64.07% in 2054. If this trend continues, most of the 1st-class agricultural lands may be lost. The study's results can assist local governments in their land management strategies and aid them in planning for the future. The results suggest that policies are necessary to control the expansion of artificial surfaces, ensuring a balanced distribution of land use.

Prediction of the dynamics of land use land cover using a hybrid spatiotemporal model in Iran

Human activities are prone to be the main drivers of land use land cover (LULC) changes, which have cascading effects on the environment and ecosystem services. The main objective of this study is to assess the historical spatiotemporal distributions of LULC changes as well as estimated future scenarios for 2035 and 2045 by considering the explanatory variables of LULC changes in Zanjan province, Iran. The LULC time-series technique was applied using three Landsat images for the years 1987, 2002, and 2019. Multi-layer Perceptron Artificial Neural Network (MLP-ANN) is applied to model the relationships between LULC transitions and explanatory variables. Future land demand was calculated using a Markov chain matrix and multi-objective land optimization in a hybrid simulation model. Validation of the model's outcome was performed using the Figure of Merit index. The residential area in 1987 was 6406.02 ha which increased to 22,857.48 ha in 2019 with an average growth rate of 3.97%. Agriculture increased annually by 1.24% and expanded to 149% (890,433 ha) of the area occupied in 1987. Rangeland showed a decline concerning its area, with only about 77% (1,502,201 ha) of its area in 1987 (1,166,767 ha) remaining in 2019. Between 1987 and 2019, the significant net change was a conversion from rangeland to agricultural areas (298,511 ha). Water bodies were 8 ha in 1987, which increased to 1363 ha in 2019, with an annual growth rate of 15.9%. The projected LULC map shows the rangeland will further degrade from 52.43% in 2019 to 48.75% in 2045, while agricultural land and residential areas would be expanded to 940,754 ha and 34,727 ha in 2045 from 890,434 ha and 22,887 ha in 2019. The findings of this study provide useful information for the development of an effective plan for the study area.

Dynamics and future prediction of LULC on Pare River basin of Arunachal Pradesh using machine learning techniques

Anthropogenic disturbances caused by increasing population densities are a significant concern as they accelerate climate change. Thus, regular monitoring of land use/land cover (LULC) is essential to mitigate these effects. Pare River basin of Arunachala Pradesh situated in the foothills of Eastern Himalayas was selected for this study. Landsat-5 TM and Landsat-8 OLI data from 2000 (T₁), 2015 (T₂), and 2020 (T₃) were used to prepare the LULC map. A support vector machine (SVM) classifier in the Google Earth Engine (GEE) environment was utilized for classification of LULC, while the TerrSet software environment was used for change analysis and projection using the CA-MC model. The SVM classifier produced overall all classification accuracies of 0.91, 0.85, and 0.91 with kappa values of 0.88, 0.82, and 0.89 for T₁, T₂, and T₃, respectively. The CA-MC model, which combines Markov chain and hybrid cellular automata, was calibrated with various predictor variables, including natural, proximity, and demographic variables along with T₁ and T₂ LULC and validated using T₃ LULC. The MLP was used for calibration, and an accuracy rate of above 0.70 was employed to generate transition potential maps (TPMs). The TPMs were used to project future LULC for 2030, 2040, and 2050. Validation analysis produced satisfactory results, with K_no, K_location, K_quality, and K_standard values of 0.96, 0.95, 0.95, and 0.93, respectively. Receiver operating characteristics (ROC) analysis showed an excellent area under the curve (AUC) value of 0.87. The findings of this study provide important insights to decision-makers and stakeholders in addressing the impacts of LULC changes.

Analysis of land use/land cover changes and prediction of future changes with land change modeler: Case of Belek, Turkey

In the areas declared to be a tourism center by state planning, a rapid tourism-related development occurs depending on the investments in tourism, which causes a dramatic land use/land cover (LULC) change. Determining, monitoring, and modeling of LULC changes are required in order to ensure the conservation-use balance and sustainability within such vulnerable areas that are under development pressure. This study consists of four steps. In the first step, the Landsat images dated 1985, 2000, 2010, and 2021 were classified using the maximum likelihood method and the LULC of Belek Tourism Center located in Turkey were determined. The second step included the identification of areal and spatial changes between the LULC classes for the four periods. In the third step, the LULC changes in Belek Tourism Center for 2040 were modeled using the land change modeler. Last step evaluated the relationship between the modeled spatial development pattern and the current planning decisions. According to the results obtained during 36 years, the rates of built-up, forest, and water body areas have increased by 11.91%, 13.67%, and 0.82%, respectively, whereas the rates of barren land and agricultural areas have reduced by 22.25% and 4.15%, respectively. The LULC map modeled for 2040 predicts the built-up areas to expand by 8.25% and the agricultural areas to shrink by 5.42% by comparison with 2021. This study will contribute as a key measure for planners, policy-, and decision-makers to make decisions related to sustainable land use in the areas declared to be a tourism center.