The impact of land use change awareness on the psychological adaptation of migrant communities in Khulna city

Urban growth is regarded as a necessary transitional stage for a sustainable economy, but uncontrolled or arbitrary urban growth rapidly consumes rural resources and causes environmental pollution, ecological deterioration. In this paper, we developed a remote sensing and GIS-based integrated approach to monitor and analyze agricultural and urban spatial land use and ecological landscape change characteristics. In the proposed approach, multi-temporal satellite images from 1995 to 2010 were selected and classified to obtain land cover and use spatial changes. And GIS was used to analyze variation tendency for land use and ecological landscape indices. Experiments were performed in the Changping County, north of Beijing to analyze rapid urbanization effects in the past two decades, especially during the Beijing 2008 Olympic Games. The results indicate that there has been a notable urban growth and a visible loss about 38.8% in cropland, meanwhile dominated landscape structures and patterns have greatly changed from agriculture to urban in the study area.

Land use/land cover change (LUCC) is an important content of geographical research on global change today, and urban land-use change is a complex dynamic and spatio-temporal process because city is not only the most concentrated region of mankind's activity, but also the most impressionable sapce interacts with the humanities factor and natural factor. Urbanization is focus of human and land's relation, which mainly performs expansions of the city space. So how to simulate this process is a key problem. In recent years, much attention is paid to urban land-use change simulations because they can betterly display the mechanism of urban land use change and make out the relevant policies. Spatial simulation on the urban land use pattern is one of the key content of LUCC. While modeling is an important tool for simulating land use pattern due to its ability to integrate measurements of changes in land cover and the associated drivers. Spatial data, like land-use data, have a tendency to be dependent (spatial autocorrelation), which means that when using spatial models, a part of the variance may be explained by neighbouring values. The classic regression model can only analyze the correlation between land use type and driving factors, but cannot depict the spatial autocorrelation. In this paper, Nanjing in Jiangsu Province being located in the Yangtze River Delta was selected as the research area for its fast development of economy, intense human activities and the obvious land use change. According to Landsat TM images of year 2003, the land use information of Nanjing city in 2003 is extracted by the unsupervised classification and manual interpreter methods, and the land use type is redivided into cultivated land, forest land, construction land and virgin land. All driving factors such as distance to town, distance to river, distance to road, population density, DEM, slope and aspect were produced with ArcGIS spatial analysis means. Then the weighting coefficient of every land use type was analyzed with SPSS13.0. The creative idea in this paper is the land use pattern in Nanjing city were investigated by means of modeling the spatial autocorrelation of land use types with the purpose of deriving better spatial land use pattern on the basis of terrain characteristics and infrastructural conditions. Through incorporating components describing the spatial autocorrelation into a classic logistic model, this paper sets up a regression model (AutoLogistic model), which considers the spatial autocorrelation factor. And use the model to simulate and analyze the spatial land use pattern in Nanjing city. In addition, the results of two different models are validated by an ROC method. The ROC can compare a map of actual land use distribution to maps of modeled probability for land use types. Through comparison with the classic logistic model without considering the spatial autocorrelation, this model showed better goodness of fitting and higher accuracy of fitting. The area under ROC curves (AUC) of arable-land, wood-land, and building-land from classic logistic regression model were 0.783, 0.824 and 0.751 respectively. The distribution of land use types of arable-land, wood-land and building-land yielded areas under the ROC curves (AUC) were improved to 0.812, 0.877 and 0.806 respectively when using Autologistic model. It is argued that the improved model based on autologistic method is reasonable in some degree, and these types of analysis can provide valuable information for modeling future land use change scenarios that need to consider local and regional conditions of actual land use, and the probability maps of land use types obtained from this study can also support government decisions on land use management for Nanjing city and similar areas. Few attempts have been made to model the pattern of urban land-use based on autologistic model. This paper proposes a new approach to predicting probability models of urban land-use pattern using the application of autologistic regression coupled with GIS, and it is an attempt of incorporating components describing the spatial autocorrelation into a classic logistic model to stimulate the urban land-use pattern, the outcomes can help to understand and explain the causes, locations, consequences and trajectories of urban land-use change, and provide a great support service for land-use planning and policy-making activities.

AN ASSESSMENT OF SPATIAL VARIATION OF LAND SURFACE CHARACTERISTICS OF MINNA, NIGER STATE NIGERIA FOR SUSTAINABLE URBANIZATION USING GEOSPATIAL TECHNIQUES

Urban land-use patterns change has become one of hot topics on land use/ land cover change (LUCC) studies in the worldwide area. As urban land change is influenced by both topography and human behavior driving factors, it is a complex dynamic and spatio-temporal progress. To simulate and analyze urban land use patterns and its driving factors, the logistic regression model is implemented as the statistical tool combined with spatial data by GIS. Because of China coastal development strategy along the Yangtze River, Nantong is one of the most fast-developing cities in the area. Meanwhile, Tongzhou District is the transitional area of Nantong main city, which is the fourth economic city of Jiangsu province located at the eastern coastal area. Based on Tongzhou land-use actual map, DEM from the SRTM and social-economic census data in the 2006 statistical year book, the land use types were mainly re-divided into construction land and cultivated land and all driving factors of topography elements, the distances to the main rivers and roads, and the densities of population and economy were implemented to each own layer with ArcGIS spatial analysis tools. And then each land-use pattern's weight coefficient is calculated by SPSS with the logistic model. Furthermore, those incorporating components of the spatial auto-correlation were also considered. As to the simulation results of Tongzhou, the area under ROC curves of construction land and cultivated land were 0.613 and 0.565 respectively, which was used to compare the land-use probability simulation with the actual map. Due to this study on the plain area, the topography driving factors played a less role than in other related studies and depressed the simulation impression, such as the study on Yongding County, which is one of the typical Karst mountain areas in northwestern Hunan province, were investigated by means of modeling the spatial autocorrelation of land use types with the purpose of deriving better spatial land use patterns on the basis of terrain characteristics and infrastructural conditions. To a certain extent, the social-economic factors mainly determined construction land distribution, and the cultivated land more depended on the surrounding main rivers and roads like Tongzhou, which is located at the eastern coastal plain. From the causes, locations and consequences of urban transitional land-use change, this study could help various local governments with land use planning and policy making.

Spatial land transformation and the psychosocial exposure among climate migrants in southwestern Bangladesh

Dynamic integration of land use changes in a hydrologic assessment of a rapidly developing Indian catchment

Population growth and land conversion will lead to an increase in water demand and land needs. The increasing need for built land will be followed by land use and land cover changes, increased land degradation, and a decrease in soil capacity for infiltration. Land use and land cover changes will affect the availability and demand of water. The objectives of this study are: (1) Analyzing the Spatial Pattern of Land Use - Land Cover Change in Ngargoyoso District in 2014 - 2023, (2) Analyzing the Availability and Water Needs of Ngargoyoso District in 2014 - 2023, (3) Analyzing the Status of Water Carrying Capacity in Ngargoyoso District in 2014 - 2023, (4) Analyzing the Effect of Land Use - Land Cover Change on Water Carrying Capacity in Ngargoyoso District in 2014 - 2023. Data collection methods include field observation, document review, and satellite imagery interpretation. The analysis used is a spatial analysis whose processing uses the Geographic Information System (GIS) Spatial analysis with GIS output of Land Use - Land Cover Change Map and Water Carrying Capacity Map of Ngargoyoso District. The results showed a significant land use - land cover change over 9 years, and there was an effect of land use - land cover change on water availability and demand in Ngargoyoso District.

Rapid land use change has taken place in many urban regions of China such as Chengdu city, many problems in society and environment have appeared. Assessment on urban land use change is essential for land sustainable development. The objective of this study is to analyze the spatio-temporal dynamic change of urban land use from 1992 to 2008 in Chengdu city, the provincial capital of Sichuan, has undergone rapid transformation during China's reform period since 1978, using Landsat TM/ETM+data in 1992, 2000 and 2008. The result shows that significant transformation in urban land use was occurred. Decrease of crop land mainly due to land use conversion to built-up land resulting in large scale urban sprawl. The converted area of cultivated land is 1.137×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> hm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , decrease area of crop land is 12.025×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> hm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , increase area of built-up land is 6.1 945×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> hm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> over the study period. The study demonstrates that the integration of RS and GIS was an effective approach for monitoring land use change. This electronic document is a “live” template. The various components of your paper [title, text, heads, etc.] are already defined on the style sheet, as illustrated by the portions given in this document.

流域内的景观格局改变是人类活动的宏观表现,会对河流水质产生显著影响,因此明确影响水质变化的关键景观因子,对于深入了解景观对水质的影响机制具有重要的研究价值。选择广东省淡水河流域为研究对象,以2007年ALOS卫星影像以及水质监测数据为基础,运用空间分析和多变量分析方法,分析淡水河流域景观格局与河流水质的相关关系。用包括流域和河岸带尺度的景观组成和空间结构信息的景观指数表征景观格局,用Spearman秩相关分析、多元线性逐步回归模型和典型相关分析(CCA)研究景观指数和水质指标的相关关系。研究结果表明:林地、城镇用地和农业用地占淡水河流域总面积超过90%,其中城镇用地超过20%。多元线性逐步回归分析和CCA结果说明水质指标受到多个景观指数的综合影响,反映了景观格局对水质的复杂影响机制。流域景观格局对河流水质有显著影响,流域尺度的景观指数比河岸带尺度的景观指数对水质影响更大。城镇用地比例是影响耗氧污染物和营养盐等污染物浓度最重要的景观指数,林地和农业用地对水质的影响较小。另外,景观破碎化对pH值、溶解氧和重金属等水质指标有显著影响。CCA的第一排序轴解释了景观指数与水质指标相关性的54.0%,前两排序轴累积能解释景观指数与水质指标相关性的87.6%,前两轴分别主要表达了城市化水平和景观破碎化水平的变化梯度。淡水河流域的景观格局特征从上游到下游呈现出城市-城乡交错-农村的景观梯度,水质变化也对应了这个梯度的变化,说明人类活动引起的流域土地覆盖及土地管理措施变化会对水质变化产生显著影响。;Water quality variation is generally linked to the change of landscape pattern in watershed, which represents the main impact of human activities in macroscopic view. Therefore, identifying the crucial landscape factors that affect water quality variation is valuable for understanding the mechanism that landscape may affect water quality. Multivariate analysis tools are effective methods to deal with complex correlations between landscape pattern and water quality. Besides, advances of remote sensing (RS) and geographic information systems (GIS) technologies have made regional and watershed scale studies much more feasible. This study was conducted along Danshui River watershed, a branch of Dongjiang River in Guangdong Province. The correlation between landscape pattern and water quality of Danshui River was represented by using spatial analysis and multivariate analysis methods base on ALOS satellite image and water quality monitoring data in 2007. Landscape metrics, including information of landscape composition and spatial configuration, were used to represent landscape pattern. In order to cover overall landscape information, landscape metrics on both watershed scale and riparian scale were used. Spearman's rank correlation analysis, multiple linear regression models with step-wise and canonical correlation analysis (CCA) were used to reveal the linkage between landscape metrics and water quality. The results show that forest, urban and agriculture land use are accounted for more than 90% of the total area in Danshui River watershed, while the area proportion of urban land exceeds 20%. Results of multiple linear regression models with step-wise and CCA showed that water quality indicators were affected by more than one landscape metric. The variation of water quality was influenced by landscape pattern significantly. The landscape metrics in watershed scale revealed more information of water quality variation than landscape metrics in riparian scale. The proportion of urban land use proportion had the greatest impact on water quality. Spearman's rank correlation analysis and multiple linear regression models showed the proportion of urban land use was the most important contributing factor to cause variation of oxygen consuming pollutants and nutrients. However, forest and agriculture land use had less influence on water quality. On the other hand, landscape metrics about landscape fragmentation were crucial factors to affect indicators of water quality, such as pH, DO and heavy metals. The result of CCA indicated that the first ordination axis could explain 54.0% of the correlations between landscape metrics and water quality indicators, and the first two ordination axes could cumulatively explain 87.6% of the correlations between landscape metrics and water quality. The result of CCA revealed that water quality had an obvious trend with the varying landscape gradient. The first two ordination axes mainly represented urbanization gradient and landscape fragmentation gradient respectively. Landscape characteristics in the study area showed a gradient of urban, urban-rural fringe, rural from upstream to downstream of Danshui River watershed. The distribution of pollutants concentration was corresponded with the gradient of landscape pattern in the watershed. Land use and cover change is an integrated result due to human activities, and change the state of eco-system of river and watershed significantly. It's highly reasonable that the water quality must correspond to the change of watershed landscape.

The article takes a fresh look at the concept of the "optimal" use of urban land. It discusses the procedure for choosing the "optimal" use of land within the context of rational spatial management practices and sets out a model solution for determining "optimal" land use types for given spatial and functional situations. A necessary set of geoinformation for informed decisions on choosing the "optimal" land use type is proposed. The study adds to the available knowledge concerning spatial analyses and simulations of "optimal" zoning processes; in doing so it applies the characteristic matrix method for inducing the optimal use of an area to diagnose the value of urban space and, in this way, to determine the "optimal" use under given circumstances. The article concludes by stating that the algorithm for selecting the "optimal” land use of an area significantly improves the decision-making process when carrying out the transformation of land use - the most important instrument for planning optimisation and organisation.

Rapid land use change has take place in Wuhan, the largest mega-city in central China during the last decade. Remotely sensed imagery together with geographical information system have long been utilized to monitor spatial and temporal land use change. The aim of this paper is to find out the land use change and the trend of urban growth in Wuhan, China using satellite images. The Landsat TM image acquired in 1991 and the Landsat ETM image acquired in 2002 were used to monitor land use change in Wuhan. The images were geo-referenced according to Gauss-Kruger projection with Krasovsky spheroid, by using 1:50, 000 topographical maps. The image processing is implemented by using Erdas Imagine package. The RMS error has been controlled under the limit of 1 pixel. The geo-referenced images were classified as seven land use types: cultivated land, forest land, grassland, urban and villages, transportation, water bodies and barren land. Two land use maps were produced for each date. The geo-referenced, classified images were compared pixel by pixel to locate and quantify land use changes that took place from 1991 to 2002 period. The further change detection analysis in a later stage is performed in ArcGIS. The transition matrix was produced and the quantitative information on the size of land use change from one type to another was compiles. The results of study indicate that the conversion of land use from cultivated land to urban was prominent, the rapid urban sprawl has occupied lots of cultivated land and water bodies, the urban area significantly increased 30%, most of which are converted from cultivated land. these valuable cultivated land need careful protection by providing land use plans to guide urban growth going toward the right directions. The results obtained from this application also indicate that the use of satellite imageries is very useful for mapping land use changes, and the monitoring land use change is essential for land use planning and urban sustainable development.

In recent years, rapid land use land cover (LULC) changes have continuously taken place in many regions all over the world as a result of human activities. In the present study, the changes in LULC were analyzed by means of multi-temporal remote sensing of Qena-Luxor Governorates in Egypt between 1984 and 2018. In order to map and monitor the land use land cover changes, several remotely sensed data were applied to create multi-maps using (1) the normalized difference vegetation index and (2) supervised classification of Landsat images using field chick and accuracy assessment, including field verification and Google Earth Professional. Therefore, the lands in the study area can be classified as follows: (1) agricultural lands, (2) built-up areas, (3) water bodies, (4) reclaimed lands, and (5) desert lands. The results indicate that agricultural lands grew from an average of 1238.7 km2 (9.8%) in 1984 to 1707.04 km2 (13.40%) in 2018 and urban lands increased from 345.2 km2 (2.7%) in 1984 to 445.28 km2 (3.5%) in 2019. Furthermore, the reclaimed lands increased approximately from 4379.7 km2 in 1984 (i.e., 34.4% of the total study area) to 4521.05 km2 in 2000 (35.507%). However, this class was followed by a marked decline to 4373.51 km2 (34.35%) between 2000 and 2010 and then increased to approximately 4442 km2 (34.89%) between 2010 and 2018. Desert lands (limestone plateau and some lowland desert fringes) decreased from 6635.4 km2 (52.2%) to 6003.5 km2 (47.15%). The results showed that the overall accuracy of the supervised classification of Landsat satellite images ranges from 87 to 92.5% while kappa statistics were from 0.83 to 90.

Background Brazil is the largest exporter of soybeans worldwide. Albeit its economic importance, soybean expansion has led to important land use and land cover changes. In this paper, we evaluate the impact of soybean expansion on ecotourism, using as a case study of the Prata River (Bonito), Brazil; tourist destination where over 30,000 tourists per year came to float in crystal waters. Methods We first evaluated land cover and land use change in the region between 2010 and 2020, checking how and where soybean plantations have expanded. Second, based on monthly data of water transparency of the Prata River, Bonito, we created five possible models considering monthly rainfall and three categories of soybean expansion (slow, rapid and medium). The models were tested through generalized linear regression analysis and ranked through AIC and AIC weight. Results Our results show that soybean expanded from occupying 4% of the river basin in 2010 to 23% in 2020, expanding mostly over pasture areas (31%) and native vegetation (12.9%). We also showed that while soybean plantation was expanding rapid between 2014 and 2016, it played a significant role in increasing the number of days the water in the Prata River was classified as very turbid. Conclusion Our results emphasize the need for soybean expansion planning, considering better management of the soil (non-tilling), common agreements between different stakeholders and the scale up of initiatives that are already in place in the region (e.g. planning of the locations of legal reserves in a way that complement the environmental protection areas (e.g. Águas de Bonito), seting aside of conservation areas ("Área Prioritária Banhados") and payment for ecosystem service schemes) . Implications for conservation Our research shows the importance of considering the different impacts soybean may have on the landscape. We present clear paths to reduce possible economic and environmental impacts, and present the importance to scale up innitiatives that are already in place in the region, such as payment for ecosystem services schemes and protection of watersheds.

Revealing the spatial–temporal pattern and convergence characteristics of urban land use efficiency has important guiding significance for adjusting and optimizing the regional urban land use structure. Taking the provincial units in China as the research object, the urban land use efficiency evaluation system considering the unexpected output was constructed, and the slack-based measure (SBA) model was used to quantitatively measure the provincial urban land use efficiency from 2000 to 2020. The exploratory spatial data analysis (ESDA) model and spatial convergence index were combined to reveal the spatial–temporal pattern and convergence characteristics of provincial urban land use efficiency. The results showed that the provincial urban land use efficiency has been continuously improving, with regional differences as shown in eastern region > northeast region > central region > western region. Moran’s I of provincial urban land use efficiency was greater than 0, there was a positive spatial correlation, and the clustering feature became increasingly significant. The spatial form of LISA was characterized by “small agglomeration and large dispersion”; the H(High)-H(High) type was clustered in the Yangtze River Delta and Beijing–Tianjin–Hebei, while the L(Low)-L(Low) type was clustered in Xizang, Xinjiang and Qinghai. There was a σ convergence in provincial urban land use efficiency, and there was significant absolute β convergence and conditional β convergence of provincial urban land use efficiency. The results showed that the differences in provincial urban land use efficiency were shrinking, showing a “catch-up effect”, and converging to their respective stable states over time. Based on the analysis of the spatial–temporal pattern and convergence characteristics of provincial urban land use efficiency in China, we could provide a direction for the optimization of the urban land use structure and efficiency improvement in China, in order to narrow the differences in urban land use efficiency in China’s four major regions.

Rapid urbanization and changing land use dynamics require robust tools for projecting and analyzing future land use scenarios to support sustainable urban development. This study introduces an integrated modeling framework that combines the Patch-generating Land Use Simulation (PLUS) model with Markov Chain (MC) analysis to simulate land use and land cover (LULC) changes for Montreal Island, Canada. This framework leverages historical data, scenario-based adjustments, and spatial drivers, providing urban planners and policymakers with a tool to evaluate the potential impacts of land use policies. Three scenarios—sustainable, industrial, and baseline—are developed to illustrate distinct pathways for Montreal’s urban development, each reflecting different policy priorities and economic emphases. The integrated MC-PLUS model achieved a high accuracy level, with an overall accuracy of 0.970 and a Kappa coefficient of 0.963 when validated against actual land use data from 2020. The findings indicate that sustainable policies foster more contiguous green spaces, enhancing ecological connectivity, while industrial-focused policies promote the clustering of commercial and industrial zones, often at the expense of green spaces. This study underscores the model’s potential as a valuable decision-support tool in urban planning, allowing for the scenario-driven exploration of LULC dynamics with high spatial precision. Future applications and enhancements could expand its relevance across diverse urban contexts globally.