A time series analysis of urbanization induced land use and land cover change and its impact on land surface temperature with Landsat imagery

Land use and land cover (LULC) change information is essential in urban planning and management. With the rapid urbanization in China, many illegal land developments have emerged in some rapidly developing regions and have caused irreversible environmental problems, posing a threat to sustainable urban development. Short-interval monitoring of LULC change therefore is necessary in these regions to control and prevent illegal land developments at an early stage.
\nConventional optical remote sensing is limited by weather conditions and has difficulties collecting timely data in tropical regions characterized by frequent cloud cover. Radar remote sensing, not affected by clouds, is therefore a potential tool for collecting timely LULC information in these regions. Polarimetric SAR (PolSAR) is more suitable than single-polarization SAR for monitoring LULC change because it can discriminate different types of scattering mechanisms. The overall objective of this study is to conduct short-interval monitoring of LULC change using RADARSAT-2 PolSAR images.
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\nClassification methods that achieve high accuracy for PolSAR images are essential in monitoring LULC change. In this study, a new method, based on the integration of polarimetric decomposition, PolSAR interferometry, object-oriented image analysis, and decision tree algorithms, is proposed for LULC classification using RADARSAT-2 PolSAR data. A comparison between the proposed classification method and Wishart supervised classification which is commonly used for the classification of PolSAR data showed that the proposed method can significantly improve LULC classification accuracy. Polarimetric decomposition, PolSAR interferometry, object-oriented image analysis, and decision tree algorithms have been determined to contribute to the improvement achieved by the proposed classification method.
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\nSelection of appropriate incidence angle is important in LULC classification using PolSAR images because incidence angle influences the intensity and patterns of radar return. Based on the proposed classification method, the present study further investigates the influence of incidence angle on LULC classification using RADARSAT-2 PolSAR images. LULC classifications using incidence angles of 31.50 and 37.56° were conducted separately. The influence of incidence angle on the classification was investigated by comparing the results of the two independent classifications. The comparison showed that large incidence angle performs much better than small incidence angle in the classification of different vegetation types, whereas small incidence angle outperforms large incidence angle in reducing the confusion between urban/built-up areas and vegetation, that between vegetable and barren land, and that among barren land, water, and lawn. Considering that the detection of urban/built-up areas and barren land is important in monitoring illegal land developments, small incidence angle is more suitable than large incidence angle in monitoring illegal land developments.
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\nChange detection methods that achieve high accuracy for PolSAR data are also essential in monitoring LULC change. The current study proposes a new method for LULC change detection using RADARSAT-2 PolSAR images. The proposed change detection method combines change vector analysis (CVA) and post-classification comparison (PCC) to detect LULC changes using object-oriented image analysis. The classification of PolSAR images is based on the proposed classification method. Compared with the PCC based on Wishart supervised classification, the proposed change detection method can achieve much higher accuracy for LULC change detection. Further investigation indicated that CVA, PCC, and object-oriented image analysis all contribute to the higher accuracy achieved by the proposed change detection method.
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\nShort-interval monitoring of LULC change was carried out using a time series of RADARSAT-2 PolSAR images. The monitoring was based on monthly LULC change detection using the proposed change detection method and appropriate incidence angle. The influence of environmental factors on short-interval monitoring of LULC change was investigated by analyzing the monthly change detection results. Paddy harvesting and planting, seasonal crop growth, and change in soil moisture and surface roughness were found to exert significant influence on the short-interval monitoring of LULC change. High accuracy can be achieved for short-interval monitoring of construction sites and bulldozed land using RADARSAT-2 PolSAR images. However, paddy harvesting and growth still cause false alarms on the monitoring of these two LULC classes.
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\nThe study indicated that conducting short-interval monitoring of LULC change using RADARSAT-2 PolSAR images is effective. High accuracy can be achieved for short-interval monitoring of construction sites and bulldozed land using the proposed change detection and classification methods, which can provide important information for the control and prevention of illegal land developments at an early stage.

Land surface temperature (LST) analysis of satellite data is critical for studying the environmental land degradation impacts. However, challenges arise to correlate the LST and field data due to the constant development in land use and land cover (LULC). This study aims to monitor, analyze, assess, and map the environmental land degradation impacts utilizing image processing and GIS tools of satellite data and fieldwork. Two thermal and optical sets of Landsat TM + 5 and TIRS + 8 data dated 1984 and 2018 were used to map the thermal and LULC changes in the Suez Canal region (SCR). The LULC classification was categorized into water bodies, urban areas, vegetation, baren areas, wetland, clay, and salt. LULC and LST change detection results revealed that vegetation and urban areas increased in their areas in 34 years. Moreover, 97% of the SCR witnessed LST rise during this period with an average rise rate of 0.352 °C per year. The most effective LULC class changes on LST were the conversions from or to baren areas, where baren areas were converted to 630.5 km2 vegetation and 104 km2 urban areas rising the LST to 43.57 °C and 45 °C, respectively. The spectral reflectance (LSR), LST profiles, and statistical analyses examined the association between LST and LULC deriving factors. In combination with field observations, five hotspots were chosen to detect and monitor natural and human land degradation impacts on LST of the SCR environment. Land degradations detected include water pollution, groundwater rising, salinity increase, sand dune migration, and seismic activity.

Multispectral remote sensing images are the primary source in the land use and land cover (LULC) monitoring. This is achieved by LULC classification and LULC change detection. The change detection in LULC includes the detection of water bodies, forest fire, forest degradation, agriculture areas monitoring, etc. Various change detection and LULC classification methods have their own advantages and disadvantages, and no single method is optimal and finds applicability for all cases. This paper summarizes and analyses the various soft computing and feature extraction techniques used for LULC classification and change detection. Based on the average error rate, performances of the different soft computing techniques are evaluated. The broad usage of multispectral remote sensing images, object-based change detection, neural networks and various levels of image fusion methods offer more potential in LULC monitoring.

The Mediterranean region of the Türkiye is among the areas most susceptible to forest fires. Although the reasons for fire outbreaks vary over time, these events consistently result in the loss of forest resources and significant ecological damage. Forest fires reduce or eliminate many forms of vegetation from the land surface. The Kavaklıdere-Muğla-Yatağan-Yılanlı fire, which occurred between 2 and 8 August 2021, affected a large area. Therefore, the study aims to investigate the land use and land cover (LULC) changes in 2020, 2021, and 2024 within Kavaklıdere district of Muğla. In this study, six LULC classes, Agriculture (A), Bare and Other (BO), Forest (F), Urban (U), Water (W) and Burnt Area (BA) were identified using Sentinel-2 satellite imagery and random forest classification technique on the Google Earth Engine (GEE) platform. In addition, land surface temperature (LST) data were obtained using the split-window algorithm applied to Landsat-8 data. The findings indicated that LULC changes are clearly in fire-affected areas, with LST values are notably higher in burned areas than in other classes. The results demonstrate the utility of remote sensing techniques for monitoring post-fire changes in land cover and surface temperature.

Spatio-temporal dynamics of land use changes of an intense anthropized basin in the Brazilian semi-arid region

The changes in land use and land cover (LULC) determine the change of the normalised difference vegetation index (NDVI) and of the land surface temperature (LST) which characterise the environment at a given moment. This study examines the LULC changes affecting the LST-NDVI relationship, using Landsat 5 Thematic Mapper (TM) and 7 Enhanced Thematic Mapper Plus (ETM+) images acquired in 1987, 2000, and 2009 in the metropolitan area of Brasov, Romania. The images were classified into seven LULC classes through the supervised classification method. NDVI maps and LST maps were obtained on the basis of the three images, atmospherically and radiometrically corrected. The relationship between LST and NDVI was analysed by linear regression for each image and for each LULC class. The results obtained show that there is a negative correlation between LST and NDVI for all the LULC classes considered together as well as for each class considered separately.

The Doyang Hydro Electric Project (DHEP) with a 75 MW power generation capacity was completed in 2000 on the Doyang river at Wokha district of Nagaland, India. This study analyses the changes in land use and land cover (LULC) and land surface temperature (LST) in the district from 1991 to 2021. Six LULC classes were analysed: forest land, water body, urban body, agricultural land, barren land, and shifting cultivation using supervised classification. The findings indicate significant LULC changes over the 30-year period. Urban areas expanded by 198.44%, from 10.42 km2 in 1991 to 31.15 km2 in 2021. Forest cover declined by 2.85%, while water bodies saw a dramatic increase of 133.3%. The regression analysis reveals a strong correlation between LULC changes and LST, with urban and barren lands contributing to higher temperatures, while forested areas, water bodies, agricultural lands, and shifting cultivation are associated with lower LST. A household survey conducted in three villages influenced by the DHEP revealed substantial socio-economic, agricultural, and environmental impacts. A paired sample t-test confirmed a significant decrease in agricultural activity, with a p-value of 0.000. These findings highlight the need for sustainable land management practices and consideration of socio-environmental impacts of large-scale infrastructure projects.

Landslide poses severe threats to the natural landscape of the Lesser Himalayas and the lives and economy of the communities residing in that mountainous topography. This study aims to investigate whether the landscape change has any impact on landslide occurrences in the Kalsi-Chakrata road corridor by detailed investigation through correlation of the landslide susceptibility zones and the landscape change, and finally to demarcate the hotspot villages where influence of landscape on landslide occurrence may be more in future. The rational of this work is to delineate the areas with higher landslide susceptibility using the ensemble model of GIS-based multi-criteria decision making through fuzzy landslide numerical risk factor model along the Kalsi-Chakrata road corridor of Uttarakhand where no previous detailed investigation was carried out applying any contemporary statistical techniques. The approach includes the correlation of the landslide conditioning factors in the study area with the changes in land use and land cover (LULC) over the past decade to understand whether frequent landslides have any link with the physical and hydro-meteorological or, infrastructure, and socioeconomic activities. It was performed through LULC change detection and landslide susceptibility mapping (LSM), and spatial overlay analysis to establish statistical correlation between the said parameters. The LULC change detection was performed using the object-oriented classification of satellite images acquired in 2010 and 2019. The inventory of the past landslides was formed by visual interpretation of high-resolution satellite images supported by an intensive field survey of each landslide area. To assess the landslide susceptibility zones for 2010 and 2019 scenarios, the geo-environmental or conditioning factors such as slope, rainfall, lithology, normalized differential vegetation index (NDVI), proximity to road and land use and land cover (LULC) were considered, and the fuzzy LNRF technique was applied. The results indicated that the LULC in the study area was primarily transformed from forest cover and sparse vegetation to open areas and arable land, which is increased by 6.7% in a decade. The increase in built-up areas and agricultural land by 2.3% indicates increasing human interference that is continuously transforming the natural landscape. The landslide susceptibility map of 2019 shows that about 25% of the total area falls under high and very high susceptibility classes. The result shows that 80% of the high landslide susceptible class is contained by LULC classes of open areas, scrubland, and sparse vegetation, which point out the profound impact of landscape change that aggravate landslide occurrence in that area. The result acclaims that specific LULC classes, such as open areas, barren-rocky lands, are more prone to landslides in this Lesser Himalayan road corridor, and the LULC-LSM correlation can be instrumental for landslide probability assessment concerning the changing landscape. The fuzzy LNRF model applied has 89.6% prediction accuracy at 95% confidence level which is highly satisfactory. The present study of the connection of LULC change with the landslide probability and identification of the most fragile landscape at the village level has been instrumental in delineation of landslide susceptible areas, and such studies may help the decision-makers adopt appropriate mitigation measures in those villages where the landscape changes have mainly resulted in increased landslide occurrences and formulate strategic plans to promote ecologically sustainable development of the mountainous communities in India's Lesser Himalayas.

Abstract. The Upper Ganga River basin is socioeconomically the most important river basin in India and is highly stressed in terms of water resources due to uncontrolled land use and land cover (LULC) activities. This study presents a comprehensive set of analyses to evaluate the population growth, LULC transformations, and water quality nexus for sustainable development in this river basin. The study was conducted at two spatial scales: basin scale and district scale. First, population data were analyzed statistically to study demographic changes, followed by LULC change detection over the period of February–March 2001 to 2012 (Landsat 7 Enhanced Thematic Mapper Plus (ETM+) data) using remote sensing and geographical information system (GIS) techniques. Trends and spatiotemporal variations in monthly water quality parameters, viz. biological oxygen demand (BOD), dissolved oxygen (DO, measured in percentage), fluoride (F), hardness (CaCO3), pH, total coliform bacteria and turbidity, were studied using the Mann–Kendall rank test and an overall index of pollution (OIP) developed specifically for this region, respectively. A relationship was deciphered between LULC classes and OIP using multivariate techniques, viz. Pearson's correlation and multiple linear regression. From the results, it was observed that population has increased in the river basin. Therefore, significant and characteristic LULC changes were observed. The river became polluted in both rural and urban areas. In rural areas, pollution is due to agricultural practices, mainly fertilizers, whereas in urban areas it is mainly contributed from domestic and industrial wastes. Water quality degradation has occurred in the river basin, and consequently the health status of the river has also changed from acceptable to slightly polluted in urban areas. Multiple linear regression models developed for the Upper Ganga River basin could successfully predict status of the water quality, i.e., OIP, using LULC classes.

Groundwater recharge is significantly influenced by anthropogenic activities, particularly changes in land use and land cover (LULC). These long-term temporal and seasonal LULC changes alter groundwater flow dynamics, necessitating their assessment for sustainable groundwater resource management. This study investigates the effects of LULC changes on groundwater recharge processes in the sub-watershed of the Nira River, Maharashtra, India. Using Google Earth Engine, LULC classifications were generated from Sentinel-2 satellite imagery acquired over a decadal period (2014–2024). A change detection algorithm was employed to decipher the long-term spatio-temporal LULC patterns, complemented by seasonal analysis using LULC maps of wet and dry months. Historical data from government agencies and private entities validated these findings, strengthening the analysis. The results indicate a 4.6% increase in built-up areas and a 5.7% decrease in forest cover over the analysis period. Rainfall data from 2015 to 2024 was correlated with groundwater level records, revealing enhanced recharge in 2024 compared to 2014. This improvement is attributed to increased rainwater harvesting structures observed during the assessment period, contributing significantly to recharge in dug wells. Seasonal LULC variations also influenced recharge dynamics, with the dry season showing higher recharge potential compared to the wet season. These findings provide critical insights into the interplay between LULC changes, groundwater recharge processes, and sustainable water resource management in the study area.Keywords: LULC, impact assessment, Groundwater recharge, Western Deccan Basalt, India

Over the past two and a half decades, rapid urbanization has led to significant land use and land cover (LULC) changes in Kabul province, Afghanistan. To assess the impact of LULC changes on land surface temperature (LST), Kabul province was divided into four LULC classes applying the Support Vector Machine (SVM) algorithm using the Landsat satellite images from 1998 to 2022. The LST was assessed using Landsat data from the thermal band. The Cellular Automata-Logistic Regression (CA-LR) model was applied to predict the future patterns of LULC and LST for 2034 and 2046. Results showed significant changes in LULC classes, as the built-up areas increased about 9.37%, while the bare soil and vegetation cover decreased 7.20% and 2.35%, respectively, from 1998 to 2022. The analysis of annual LST revealed that built-up areas showed the highest mean LST, followed by bare soil and vegetation. The future simulation results indicate an expected increase in built-up areas to 17.08% and 23.10% by 2034 and 2046, respectively, compared to 11.23% in 2022. Similarly, the simulation results for LST indicated that the area experiencing the highest LST class (≥ 32 °C) is expected to increase to 27.01% and 43.05% by 2034 and 2046, respectively, compared to 11.21% in 2022. The results indicate that LST increases considerably as built-up areas increase and vegetation cover decreases, revealing a direct link between urbanization and rising temperatures.

Time series analysis of land use and land cover changes related to urban heat island intensity: Case of Bangkok Metropolitan Area in Thailand

Assessment of LULC change dynamics and its relationship with LST and spectral indices in a rural area of Bengaluru district, Karnataka India

Application of multi layer perceptron neural network Markov Chain model for LULC change detection in the Sub-Himalayan North Bengal

Unplanned local development and alterations in Land Use and Land Cover (LULC) are an early alert for towns and cities around the world. The population pressure has resulted in the conversion of natural land into impermeable areas, which has altered the surface energy budget and created microclimatic differences locally. The main objective of the present study is to estimate LULC changes and its relation to increases Land Surface Temperature (LST) in the Haldia Industrial Region (HIR) of West Bengal using multi-temporal Landsat 5 Thematic Mapper (TM) of 1992 and Landsat 8 Operational Land Imager (OLI) of 2022 with 30-m spatial resolution images were obtained from the USGS website. The supervised classification method was applied by using Maximum Likelihood classification (MLC) to classify the satellite images into various LULC classes such as settlement, waterbody, vegetation, agriculture land, and fallow land, respectively. The result suggested an overall increase of settlement area from 13.49 km2 in 1992 to 44.54 km2 in 2022. On the other hand, the agriculture land decreased from 214.26 km2 to 152.34 km2 due to fast urbanization, decline green lands, and expansion of barren lands. The outcome result from LST was exhibited that the LST value has increased 3.77°C during 1992 to 2022 in this study area. The Normalized Difference Vegetation Index (NDVI) was showed a strong negative relationship with LST with a determination coefficient (R2) values found as 0.491 (1992) and 0.356 (2022). The positive determination coefficient between LST with Normalized Difference Water Index (NDWI) was found as 0.0002 (1992), and 0.0417 (2022). R2 values were estimated as 0.174 (1992), and 0.0347 (2022) between LST with Normalized Difference Built-up Index (NDBI) that indicated a positive determination coefficient. The findings enhanced understanding of the relationship between urban LST and LULC in developing an inclusive climate resilience policy and making the HIR more sustainable to the effects of climate change.