Land Use Analysis Research Articles

Analysing Land Use and Cover Transformations in Berhampore, West Bengal, India: A CA-Markov and ANN Simulation Approach for Future Predictions

Analysing Land Use and Cover Transformations in Berhampore, West Bengal, India: A CA-Markov and ANN Simulation Approach for Future Predictions

Analyses of LULC dynamics in a socio-ecological system of the Bale Mountains Eco Region of Southeast Ethiopia.

Analysis of land use and land cover (LULC) change and its drivers and impacts in the biodiversity hotspot of Bale Mountain's socio-ecological system is crucial for formulating plausible policies and strategies that can enhance sustainable development. The study aimed to analyze spatio-temporal LULC changes and their trends, extents, drives, and impacts over the last 48 years in the Bale Mountain social-ecological system. Landsat imagery data from the years 1973, 1986, 1996, 2014, and 2021 together with qualitative data were used. LULC classification scheme employed a supervised classification method with the application of the maximum likelihood algorithm technique. In the period between 1973 and 2021, agriculture, bare land, and settlement showed areal increment by 153.13%, 295.57%, and 49.03% with the corresponding increased annual rate of 1.93%, 2.86%, and 0.83%, respectively. On the contrary, forest, wood land, bushland, grass land, and water body decreased by 29.97%, 1.36%, 28.16%, 8.63%, and 84.36% during the study period, respectively. During the period, major LULC change dynamics were also observed; the majority of woodland was converted to agriculture (757.8 km2) and grassland (531.3 km2); and forests were converted to other LULC classes, namely woodland (766.5 km2), agriculture (706.1 km2), grassland (34.6 km2), bushland (31.9 km2), settlement (20.5 km2), and bare land (14.3 km2). LULC changes were caused by the expansion of agriculture, settlement, overgrazing, infrastructure development, and fire that were driven by population growth and climate change, and supplemented by inadequate policy and institutional factors. Social and environmental importance and values of land uses and land covers in the study area necessitate further assessment of potential natural resources' user groups and valuation of ecosystem services in the study area. Hence, we suggest the identification of potential natural resource-based user groups, and assessment of the influence of LULC changes on ecosystem services in Bale Mountains Eco Region (BMER) for the sustainable use and managements of land resources.

Identification and Analysis of Long-Term Land Use and Planting Structure Dynamics in the Lower Yellow River Basin

Accurate identification of the spatio-temporal planting structure and analysis of its driving factors in an irrigation district are the important bases for scientific and reasonable utilization of irrigation water resources. In pursuit of this goal, the training sample migration method combined with the random forest algorithm were used to classify land use and planting structure over 2001–2022 in the lower Yellow River Basin. Moreover, an econometric regression model was applied to quantify the driving factors of the change in the crop-planted area. The results illustrated that the identification method has relatively high accuracy in identifying historical periods of land use and planting structures, with the average kappa coefficient equating to 0.953. From 2001 to 2022, the area of cultivated land was the largest, with the proportion of the total area increasing from 45.72% to 58.12%. The planted area of winter wheat–summer maize rotation increased from 74.84% to 88.11% of the cultivated land. While the planted area of cotton declined by 96.36%, about 50% of cotton planting was converted to the winter wheat–summer maize rotation planting. The government policies about grain purchase and storage were the dominant factors for the change in the crop-planted area. This resulted in an increase of 63.32 × 103 ha and 63.98 × 103 ha in the planted area of winter wheat and summer maize, respectively. The findings are of great significance to the allocation of water resources in irrigation districts of the lower Yellow River Basin.

Traffic Management on The Cikereteg-Pancawati Road in The Caringin District Tourism Area Bogor District

Traffic as part of transportation planning is a vital indicator of tourism development. Bogor Regency is one of the favorite tourist destinations in Jabodetabek. One of the tourist attractions in Bogor Regency is natural tourism around the Puncak Bogor area. This study aims to analyze land use around Jalan Cikereteg-Pancawati, analyze the performance and level of traffic service on the road, and conduct a SWOT analysis for traffic management strategies. The study used GIS to analyze changes in land use and analyzed the level of road service on Jalan Cikereteg-Pancawati. The traffic performance analysis was conducted by analyzing the volume values and degrees of saturation on Sundays and Thursdays. The land use analysis showed that the area is dominated by forests, plantations, and rice fields, indicating great potential as a tourist area. The traffic performance analysis revealed that Sundays have higher volume values and degrees of saturation than Thursdays, with the highest degree of saturation occurring on Sunday segment 3. The study concludes that the traffic management strategy for Jalan Cikereteg-Pancawati should focus on managing pavement repairs and road widening, providing bus and car parking, and utilizing available human resources and supporting regulations for the development of Bogor Regency as a natural tourism area. The study's findings suggest that effective traffic management is crucial for the sustainable development of natural tourism in Bogor Regency. The proposed traffic management strategies can help alleviate traffic congestion and improve the overall tourist experience, ultimately contributing to the region's economic growth and social well-being.

Research on Sustainable Land Use in Alpine Meadow Region Based on Coupled Coordination Degree Model—From Production–Living–Ecology Perspective

Changes in land use types in alpine meadow areas have significant impacts on the ecological environment in alpine areas. Exploring land use change is crucial for land use management and optimization in alpine regions. Thus, it is necessary to analyze land use evolution and its drivers in alpine meadow regions from a production–living–ecology space (PLES) perspective by using remote sensing data. We first constructed the PLES evaluation system for Gannan. Then, we analyzed the spatial and temporal evolution characteristics and coupling degree of PLES in the study area. Finally, the driving factors affecting PLES were explored with geodetector. The conclusions of the study reveal that the distribution of productive and ecological spaces is large and concentrated, while the distribution of living spaces is more decentralized. The PLES was mainly concentrated in the area above 2500 m but below 4000 m and with a slope of 40° or less. During the study period, the area of production space showed a decreasing trend, while the areas of living and ecological space both showed increasing trends, primarily occurring at the expense of production space. DEM and GDP were the main factors affecting the distribution of PLES. The coupling level and the degree of coupling coordination were relatively stable in general, showing a pattern of “high in the east and low in the west”. The study provides technical support and a theoretical basis for the future planning of land space and ecological environment optimization in the alpine meadow regions.

Temporal analysis of land use in Guajará, Amazonas, Brazil

Environmental degradation in the Amazon is a recurring theme in environmental management and geography studies, especially when the focus is turned to analyzing agricultural production and its continuous association with deforestation. The purpose of this study is to analyze land use and land cover in the municipality of Guajará, Amazonas between 1985 and 2022, in order to verify the growth dynamics of the area used for anthropogenic purposes using data from the 1995-1996 and 2006 IBGE agricultural censuses and the MAPBIOMAS project. The methodology is based on the theoretical framework and consists of processing and mapping the MAPBIOMAS use classes and statistical processing of the agricultural census data. The result shows a significant increase in the area of the pasture class, in two axes of growth to the southern portion of the municipality, due to its strong economic relationship with the state of Acre

Analyzing land use and climate change impacts of Suğla water storage in Turkey

Analyzing land use and climate change impacts of Suğla water storage in Turkey

Analyzing Land Use and Land Cover Dynamics Using Geospatial Approaches: A Case Study of District Lahore, Pakistan

The present study focuses on the city of Lahore due to the substantial impacts of LULC on land-atmosphereclimatic interactions. This study aims to find and interpret the variations in land use patterns in Lahore during 20 years(2003, 2013, and 2023) with a ten-year interval. For in-depth analysis, four different classes- built-up, barren land,vegetation, and water bodies were determined. Several pre- and post-processing procedures were carefully carried outto improve the accuracy of the results after Landsat 7, Landsat 8, and Sentinel 2 satellite imageries had been utilized.The methodology included supervised classification using the maximum likelihood algorithm along with changedetection to evaluate and interpret the LULC alterations. The extent of change is demonstrated by quantitative datashowing that the built-up area increased from 308.6491261 sq. km in 2003 to 336.9702759 sq. km in 2023. On theother hand, there was a drop of 1099.13587 sq. km in barren land and a decrease of 10.47416794 sq. km in waterbodies. Despite a notable rise between 2003 and 2013, vegetation cover decreased by 328.4721624 sq. km by 2023.This study offers important insights for sustainable urban development and land management policy in addition tofurthering our understanding of the dynamics of land use and land cover in Lahore

Investigating the spatial interaction between urban expansion and the regional thermal environment in Guangxi Beibu Gulf urban agglomeration of China

Rapid urbanization and its associated surface modifications play a vital role in generating and intensifying the urban heat island (UHI). Numerous research has investigated the impact of landscape composition and structure on urban land surface temperature (LST), yet the spatial clustering and heterogeneity between urbanization and the regional thermal environment remained largely unexplored. Here, we analyzed land use dynamics and LST variations in the Guangxi Beibu Gulf urban agglomeration (GBG_UA) from 2000 to 2020. The spatial relationships between urban expansion and the regional thermal environment were explored by bivariate spatial autocorrelation and coupling coordination degree analysis. The results demonstrated that the land use in the GBG_UA remained relatively stable, except for the expansion of developed land in urban centers. The area of heat island zones clearly increased, leading to an enhancement of the UHI effect. Urban development and the regional thermal environment exhibited a robust positive spatial autocorrelation. The H–H (High-High) and L-L (Low-Low) types displayed significant aggregation effects. The H–H cluster areas dominated the central urban cores of prefecture-level cities, while the L-L correlation areas were primarily found in mountainous regions. The overall coupling coordination degree was relatively low, with most areas falling into the moderate dissonance level. While it showed a positive shift, with the regions of concordance levels expanding and coupling coordination degree increased. Our study reveals a close spatial correlation between urban sprawl and the UHI effect. The results can offer valuable insights for optimizing the urban green infrastructure to enhance urban heat mitigation and adaptation.

Spatial Analysis of LULC Changes and Suitability of Ngawi Regency on Spatial Regional Planning 2011-2031 Using Geographic Information System

Abstract This study analyzes land use change in Ngawi Regency, East Java, in 2011 and 2021 and examines the suitability of land use, Regional Spatial Plan, and Regional Medium-Term Development Plan. Qualitative descriptive analysis methods and spatial-temporal analysis are used to identify land-use change, land-use change, and land-use-related problems. The results showed a discrepancy between actual land use and RTRW in several sub-districts, such as Padas, Pangkur, Ngawi, Pitu, Kedunggalar, Ngrambe, Sine, Jogorogo, Kendal. This discrepancy includes changes in land functions from settlements to industries, tourism areas to settlements, industries to settlements, and vice versa. The total area of nonconformity reached 40,361 hectares or 28.93 % of the Ngawi area. GIS and map overlays provide an in-depth understanding of land change patterns, with settlements and industrial sectors being key focuses. Although the Regional Spatial Plan has been established to regulate land use, its implementation still needs to be improved in 2021. The development of tourism, industrial, agricultural, and residential areas sometimes goes differently than planned. This research highlights the need for coordination between Regional Medium-Term Development Plans and Regional Spatial Plans to achieve sustainable regional development.

Land Use Change Analysis and Remote Sensing-Based Spatial Evaluation In Kendal District, Indonesia in 2015 and 2020

Abstract Land use change in Kendal District, Indonesia, has become a crucial issue in spatial management. The mismatch between land use and spatial planning is one of the problems that often occurs. Limited land and rapid population growth have driven significant changes in land use, especially in the industrial sector, so an evaluation is needed by comparing actual land use in the area with the applicable Spatial Plan. This research aims to analyze land use change between 2015 and 2020 in Kendal District. Based on quantitative approaches and descriptive analysis, secondary data and interpretation of remote sensing imagery are used to understand land change. The results show that there were significant changes in spatial planning and land use in 2015 and 2020, so these results can make an important contribution to the government in regional planning and sustainable urban development. Changes in land use in Kendal Regency have not been in accordance with the Spatial Pattern Plan Map of the Kendal area. This is because changes will continue to occur, so changes in land use will also continue to change. It is not surprising that changes have not occurred significantly or in accordance with the planned pattern.

Geospatial Analysis of Land Use/ Land Cover Changes in the Northern Province, Sri Lanka

Geospatial Analysis of Land Use/ Land Cover Changes in the Northern Province, Sri Lanka

Disentangling the Influential Factors Driving NPP Decrease in Shandong Province: An Analysis from Time Series Evaluation Using MODIS and CASA Model

Net Primary Productivity (NPP) is a critical metric for assessing terrestrial carbon sequestration and ecosystem health. While advancements in NPP modeling have enabled estimation at various scales, hidden anomalies within NPP time series necessitate further investigation to understand the driving forces. This study focuses on Shandong Province, China, generating a high-resolution (250 m) monthly NPP product for 2000–2019 using the Carnegie–Ames–Stanford Approach (CASA) model, integrated with satellite remote sensing and ground observations. We employed the Seasonal Mann–Kendall (SMK) Test and the Breaks For Additive Season and Trend (BFAST) algorithm to differentiate between gradual declines and abrupt losses, respectively. Beyond analyzing land use and land cover (LULC) transitions, we utilized Random Forest models to elucidate the influence of environmental factors on NPP changes. The findings revealed a significant overall increase in annual NPP across the study area, with a moderate average of 503.45 gC/(m2·a) during 2000–2019. Although 69.67% of the total area displayed a substantial monotonic increase, 3.89% of the area experienced abrupt NPP losses, and 8.43% exhibited gradual declines. Our analysis identified LULC transitions, primarily driven by urban expansion, as being responsible for 55% of the abrupt loss areas and 33% of the gradual decline areas. Random Forest models effectively explained the remaining areas, revealing that the magnitude of abrupt losses and the intensity of gradual declines were driven by a complex interplay of factors. These factors varied across vegetation types and change types, with explanatory variables related to vegetation status and climatic factors—particularly precipitation—having the most prominent influence on NPP changes. The study suggests that intensified land use and extreme climatic events have led to NPP diminishment in Shandong Province. Nevertheless, the prominent positive vegetation growth trends observed in some areas highlight the potential for NPP enhancement and carbon sequestration through targeted management strategies.

Analysis of the Small Business Land Use in the Agribusiness Industry of the Voronezh Region

Analysis of the Small Business Land Use in the Agribusiness Industry of the Voronezh Region

Generation and classification of patch-based land use and land cover dataset in diverse Indian landscapes: a comparative study of machine learning and deep learning models.

In the context of environmental and social applications, the analysis of land use and land cover (LULC) holds immense significance. The growing accessibility of remote sensing (RS) data has led to the development of LULC benchmark datasets, especially pivotal for intricate image classification tasks. This study addresses the scarcity of such benchmark datasets across diverse settings, with a particular focus on the distinctive landscape of India. The study entails the creation of patch-based datasets, consisting of 4000 labelled images spanning four distinct LULC classes derived from Sentinel-2 satellite imagery. For the subsequent classification task, three traditional machine learning (ML) models and three convolutional neural networks (CNNs) were employed. Despite facing several challenges throughout the process of dataset generation and subsequent classification, the CNN models consistently attained an overall accuracy of 90% or more. Notably, one of the ML models stood out with 96% accuracy, surpassing CNNs in this specific context. The study also conducts a comparative analysis of ML models on existing benchmark datasets, revealing higher prediction accuracy when dealing with fewer LULC classes. Thus, the selection of an appropriate model hinges on the given task, available resources, and the necessary trade-offs between performance and efficiency, particularly crucial in resource-constrained settings. The standardized benchmark dataset contributes valuable insights into the relative performance of deep CNN and ML models in LULC classification, providing a comprehensive understanding of their strengths and weaknesses.

Land use inspection technique affects land surface temperature from satellite data

This research aims to present a land use inspection technique that affects land surface temperature from satellite data by using Maha Sarakham province, Thailand as a case study. Procedures: 1) Analyze land use from Sentinel-2 Satellite data that can be divided into 4 categories such as water, agriculture, forest, and urban, 2) Analyze land surface temperature from Sentinel-3 Satellite data. The study results found that urban area has the highest average surface temperature followed by forest area, water area, and agricultural area respectively. The analyzed data from the satellite found the highest average surface temperature is 32.20°C. The result shows that this inspection technique can analyze land surface temperature in other areas of the countryThis research aims to present a land use inspection technique that affects land surface temperature from satellite data by using Maha Sarakham province, Thailand as a case study. Procedures: 1) Analyze land use from Sentinel-2 Satellite data that can be divided into 4 categories such as water, agriculture, forest, and urban, 2) Analyze land surface temperature from Sentinel-3 Satellite data. The study results found that urban area has the highest average surface temperature followed by forest area, water area, and agricultural area respectively. The analyzed data from the satellite found the highest average surface temperature is 32.20°C. The result shows that this inspection technique can analyze land surface temperature in other areas of the country.

Estimation of Spatiotemporal Changes of Land Cover in Llapi River Catchment (Northeastern Part of Kosova)

Aim of the studyIn recent decades, Kosova in general and the Llapi River catchment have experienced landscape transformation which resulted in Land Use and Land Cover (LULC) changes, mainly in flat terrains where most of country’s population is concentrated. Other landscapes have experienced changes too, but on a small scale. The aim of the study was to identify landscape transformation and driving forces behind it.Material and methodsSpatiotemporal changes of Land Use and Land Cover (LULC) were made using GIS techniques. Land Cover data from Copernicus Land Monitoring Service (CLMS) with spatial resolution 100 m for three different years: 2000, 2006 and 2018 were analyzed. DEM with 10 m spatial resolution was used to estimate slope steepness and catchments’ altitude. Population data for settlements inside the Llapi River catchment represented in features were downloaded from the Statistical Agency of Kosova, and those data include population numbers for official censuses between 1948 until 2011, while ArcMap 10.8 environment was used for analysis.Results and conclusionsWith GIS technique in analyzing Land Use and Land Cover data (LULC) and other open source data, we found that agricultural land have decreased in area by 9.53%, while the increase of artificial surface is 160.87%, or expressing by absolute values, total agricultural areas in 2000 were 37,827 ha, while in 2018 were 34,334 ha, meaning 0.3ha per capita, less than European level. In order to protect agricultural land, zoning and land use plans should be implemented.

Evaluating the impact of Zanjan city’s economic drivers on the quality of life in peri-urban villages: a case study of Do Asb and Payin Kouh villages

The interplay between urban centers and peri-urban villages plays a pivotal role in rural development, particularly in the economic dimension. Analyzing these relationships and their influence on the progress and prosperity of peri-urban villages, coupled with capacity building, empowerment, and the integration of urban-rural management, is essential to reduce disparities and promote spatial justice, aligning with the sustainable development of rural areas. This study aims to assess the effects of Zanjan city's economic drivers on the livability quality of Do Asb and Payin Kouh, two peri-urban villages situated in Zanjan city. Employing a descriptive-analytical and practical approach, the study's statistical population comprises the aforementioned villages in Zanjan city. A sample of 219 households was selected from among the sample population based on Cochran's formula. Data collection involved both library research and fieldwork, including the use of questionnaires. Descriptive and inferential statistics (multivariate regression) were employed for data analysis, along with descriptive statistics (averages) and geographic information systems to analyze land use changes. The research findings unveiled a robust correlation (0.87) between the quality of life in peri-urban villages and the economic drivers of Zanjan city. This indicates a significant and positive relationship between Zanjan city's economic drivers (independent variable) and the quality of life in peri-urban villages (dependent variable). Among the examined variables, communication flow for purchases, with a coefficient of 0.28, contributed most to the variance analysis of the quality of life in peri-urban villages. In contrast, the investment flow of urban residents in village economic activities and the flow of money exchange had relatively lower coefficients of 0.13 and 0.14, respectively. Thus, the research underscores the considerable impact of Zanjan's economic drivers on enhancing the quality of life in peri-urban villages and highlights the pivotal role of rural-urban links in fostering sustainable rural development.

Comprehensive Analysis of Landform and Land Use/ Land Cover Mapping of Waghora Watershed Using Sentinal-2B and SRTM-DEM Data

This paper aims to integrate the use of high spectral resolution a remote sensing data of Sentinel-2B imagery along with the Digital Elevation Model (DEM) derived from the SRTM stereo data to delineate the landforms and analyze the land use and land cover in Waghora watershed, Sausar tehsil, Chhindwara district, situated on basaltic terrain in northern Deccan plateau. A 30 m resolution DEM of the study area was generated, capturing terrain parameters like elevation, slope, aspect, hillshade, and drainage. Sentinel-2B (10 m) images from two seasons were collected FCC (false colour composite) and were prepared by ARCGIS software. combination of Sentinel-2B imagery, SRTM-DEM data, and ground truth verifications to delineate eight major landforms in the studied area. Slope information, extracted from high-resolution DEMs, reveals 4 distinct slope classes. The landforms, including Plateau, Escarpment, Foot Slope, Upper Pediment, Lower Pediment, Valley, Interfluve, and Mound, are characterized based on visual interpretation and geological context. Each landform exhibits unique features, elevation ranges, and dominant land use/cover types, such as single crop cultivation, forest, or degraded land. This detailed terrain analysis aids in land-use planning and environmental management initiatives. The land use/land cover analysis utilizes Sentinel-2B data to identify five classes: degraded forest, double crop, single crop, orchard, and land with and without scrubs. Dense forest dominates 24.2% of the watershed, while double crop and single crop lands cover 36.7% and 24.2% respectively. Orchards occupy a minimal 0.8% of the area, and wastelands account for 5.7%. The relationship between landforms, slope, and land use/cover reveals dominant forestation on steep escarpments, while valleys and interfluves support double cropping due to gentle slopes and depositional processesThe study elucidated connections to examine diverse biophysical phenomena. It highlights the effectiveness of employing SRTM-DEM and Sentinel-2B imagery for analyzing geomorphic characteristics and mapping land use/land cover, offering significant perspectives for land management and planning approaches.

Changes in Runoff Coefficient Index as a Result of Land Use Change (Case Study: Malang City)

The result of this research is to present observations about changes in the value of the conveyance coefficient in an area or region caused by changes in land use as a basis for decision making for water resources management in urban areas, by considering field data and satellite image classification to observe changes. As well as the consequences of the development of an area as seen from population growth, economy and housing needs. Malang City continues to experience land use change as evidenced by the decrease in agricultural land area. The method used is supervised classification with the help of geographic information systems and analyzing land use change over the past 20 years through map overlay. The data used are Landsat and Terrasar-X satellite images with the land use classes used are open land, settlements, agriculture, and shrubs. Satellite images were classified to analyze the extent of land use change and to see the rate of change in the conveyance coefficient of each class over the past 20 years. The results show that the study area has a change in the value of the conveyance coefficient caused by a fairly high land use change from agricultural land to built-up land such as settlements or housing. Malang City has a tendency to increase the population significantly because it is an educational city that continues to need a place to live and allows the rotation of various economic needs. The results of the analysis showed that there were changes in the value of the conveyance coefficient from 2002 to 2022, with an upward trend. In 2002 the C value was 0.2468; in 2007 it was 0.2899; in 2012 it was 0.3457; in 2017 it was 0.3895; in 2022 it was 0.4119. With an average value of change every year is about 13.78%.