Urban Land Use And Land Cover Research Articles

Spatial Diversity Changes in Land Use and Land Cover Mix in Central European Capitals and Their Commuting Zones from 2006 to 2018

This article investigates the land use and land cover (LULC) mosaic as a function of distance to the city centre. The research area is four Functional Urban Areas (FUAs) of the capitals of Central European countries: Czechia (Prague), Poland (Warsaw), Slovakia (Bratislava), and Hungary (Budapest). The article presents LULC mix changes in these FUAs in the context of transformations in urban cores and commuting zones of capital city metropolises, which have large populations and prominent positions in the country or region. The study makes use of Urban Atlas LULC data for 2006, 2012, and 2018. LULC change was analysed using a hexagonal tessellation with the hexagon as the basic spatial unit. Spatial entropy (ENT) and Simpson’s Diversity Index (SIDI) were employed. The change in ENT and SIDI were determined as a function of distance to urban cores, as well as changes on the first level of Urban Atlas nomenclature for 2006–2012 and 2012–2018, as well as on the third level of nomenclature for 2012–2018. The research shows that changes from 2006 to 2012 were more considerable than from 2012 to 2018. It also revealed that, if LULC classes are considered on the first level of nomenclature, diversity in urban cores grows and then declines in commuting zones. An analysis of diversity on the third level of nomenclature demonstrated its decline with the growing distance to the urban core. It has also been demonstrated that the mean values of ENT and SIDI are approximately twice as high in urban cores as in commuting zones, indicating a plateau of the mean value of ENT and SIDI over the study period in urban cores compared to an increase in commuting zones around them. The conducted research will be helpful to urban planners and decision-makers in directing the further, inevitable development of metropolitan areas in accordance with sustainable environmental management.

Transfer-Ensemble Learning: A Novel Approach for Mapping Urban Land Use/Cover of the Indian Metropolitans

Land use and land cover (LULC) classification plays a significant role in the analysis of climate change, evidence-based policies, and urban and regional planning. For example, updated and detailed information on land use in urban areas is highly needed to monitor and evaluate urban development plans. Machine learning (ML) algorithms, and particularly ensemble ML models support transferability and efficiency in mapping land uses. Generalization, model consistency, and efficiency are essential requirements for implementing such algorithms. The transfer-ensemble learning approach is increasingly used due to its efficiency. However, it is rarely investigated for mapping complex urban LULC in Global South cities, such as India. The main objective of this study is to assess the performance of machine and ensemble-transfer learning algorithms to map the LULC of two metropolitan cities of India using Landsat 5 TM, 2011, and DMSP-OLS nightlight, 2013. This study used classical ML algorithms, such as Support Vector Machine-Radial Basis Function (SVM-RBF), SVM-Linear, and Random Forest (RF). A total of 480 samples were collected to classify six LULC types. The samples were split into training and validation sets with a 65:35 ratio for the training, parameter tuning, and validation of the ML algorithms. The result shows that RF has the highest accuracy (94.43%) of individual models, as compared to SVM-RBF (85.07%) and SVM-Linear (91.99%). Overall, the ensemble model-4 produces the highest accuracy (94.84%) compared to other ensemble models for the Kolkata metropolitan area. In transfer learning, the pre-trained ensemble model-4 achieved the highest accuracy (80.75%) compared to other pre-trained ensemble models for Delhi. This study provides innovative guidelines for selecting a robust ML algorithm to map urban LULC at the metropolitan scale to support urban sustainability.

Comparison of accuracy and reliability of random forest, support vector machine, artificial neural network and maximum likelihood method in land use/cover classification of urban setting

Accurate land use and land cover (LULC) is crucial for sustainable urban planning and for many scientific researches. However, the demand for accurate LULC maps is increasing; it is required to compare the classification algorithms to choose the best one. Though, machine and deep learning algorithms are widely used across the world their application is limited in Bangladesh. Accurate urban LULC mapping is challenging because urban heterogeneity affects image classification models in specific feature extraction. In this research, the accuracy of machine learning algorithms (MLA) of RF (Random Forest), SVM (Support Vector Machine), deep learning algorithm (DLA) of ANN (Artificial Neural Network) and traditional Maximum Likelihood (MaxL) method was compared in LULC classification of Dhaka city. Model accuracy of MLA and DLA was tested by statistical indices of sensitivity, specificity, precision, recall F1 etc. There is a high correlation between SVM and ANN models were found. The overall accuracy of the maps was 0.93, 0.94, 0.91 and 0.95 and kappa was 0.89, 0.91, 0.86 and 0.93 for the MaxL, RF, SVM and ANN models respectively. The user accuracy and producer accuracy largely varied according to LULC classes in the applied models. The lowest accuracy of the models was found for bare land classification followed by built-up and vegetation. The high mixture of LULC classes affects the accuracy of built up and bare land classification which produces the lowest accuracy in the MaxL model. The findings indicate that the most accurate and reliable model for urban LULC classification was the ANN model.

Impact of land use changes on the land surface thermal environment in Nanchang, Jiangxi province, China

Throughout human history, human activities have resulted in land use and land cover changes (LUCC) and can have a direct impact on the land surface thermal environment (LSTE). In the existing studies, the holistic nature of changes in land use and land cover (LULC) has been neglected in favor of focusing on the interactions between different LULC types and the land surface thermal environment. This study used ArcGIS Pro 3.0, ENVI 5.3, and SPSS software to construct a contribution index model and stepwise regression equation to investigate the relationship between LULC type, structure, and pattern changes and LSTE changes in Nanchang City, Jiangxi Province, China, from 1990 to 2020. The findings revealed that 1) rapid urbanization has led to a surge in the area of built-up land and a decrease in the area of arable land in Nanchang; between 1990 and 2020, the area of built-up land in Nanchang increased by 433.29 km2, while the area of arable land decreased by 291.99 km2; 2) The land surface temperature (LST) was divided into five classes according to the equal spacing method, and the areas with the highest and lowest temperature classes were the high temperature zone and the low temperature zone, respectively. Over the past 30 years, the LSTE in Nanchang has gradually deteriorated, with the area of the low temperature zone shrinking by 554.2 km2 and the area of other classes appearing to increase significantly; 3) the contribution index and stepwise regression equation demonstrate that the primary reasons for the worsening of the LSTE are an increase in the scale of cultivated land and construction land. It was found that rationalization of urban LULC type, structure, and pattern can effectively reduce land surface temperature.

Understanding the linkages between spatio-temporal urban land system changes and land surface temperature in Srinagar City, India, using image archives from Google Earth Engine.

Land-use and land-cover (LULC) is an important component for sustainable natural resource management, and there are considerable impacts of the rapid anthropogenic LULC changes on environment, ecosystem services, and land surface processes. One of the significant adverse implications of the rapidly changing urban LULC is the increase in the Land Surface Temperature (LST) resulting in the urban heat island effect. In this study, we used a time series of Landsat satellite images from 1992 to 2020 in the Srinagar city of the Kashmir valley, North-western Himalaya, India to understand the linkages between LULC dynamics and LST, derived from the archived images using the Google Earth Engine (GEE). Furthermore, the relationship between LST, urban heat island (UHI), and biophysical indices, i.e., Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI), was also analysed. LULC change detection analysis from 1992 to 2020 revealed that the built-up area has increased significantly from 12% in 1992 to 40% in 2020, while the extent of water bodies has decreased from 6% in 1992 to 4% in 2020. The area under plantations has decreased from 26% in 1992 to 17% in 2020, and forests have decreased from 4 to 2% during the same period. Urban sprawl of Srinagar city has resulted in the depletion of natural land covers, modification of natural drainage, and loss of green and blue spaces over the past four decades. The study revealed that the maximum LST in the city has increased by 11°C between 1992 and 2020. During the same period of time, the minimum LST in the city has increased by 5°C, indicating the impact of urbanization on the city environment, which is reflected by the observed changes in various environmental indices. UHI impact in the city is quite evident with the maximum LST at the city centre having increased from 13.03°C in 1992 to 22.01°C in 2020. The findings shall serve as a vital source of knowledge for urban planners and decision-makers in developing sustainable urban environmental management strategies for Srinagar city.

Simulating future land-use within the uThukela and uMngeni catchments in KwaZulu-Natal

Due to anthropogenic activities, the earth's surface is consistently being altered. These alterations take the form of Land-use/cover change (LULCC), which is a fundamental driver of global, regional and local environmental change. LULCC studies have become pivotal in supplementing our understanding and observations of environmental change. However, understanding the past and present spatial-temporal variability of LULCC characteristics and their link to future land-use/cover trajectories at a catchment scale is limited, particularly in Southern Africa. To address this limitation, this study simulated future land-use change utilising a spatially distributed, empirical land-use modelling approach for the uThukela and uMngeni catchments in the KwaZulu-Natal province of South Africa. The CA-Markov model, a popular and frequently utilised model employed in land-use and land-cover (LULC) predictive modelling, was selected to simulate LULCC conjointly with Geographic Information Systems (GIS) techniques. The obtained kappa values (Kstandard, Klocation and Kno) achieved during the validation were all above 80%, thus indicating the model's reliability and capability to successfully predict future LULC in the study sites. Future projections indicated that both study areas are anticipated to experience anthropogenic induced LULCC, which further fragments the landscape configuration, functionality and ecological stability. Historical analysis of LULCC between 1990 and 2018, in the study catchments revealed considerable declines in the areas under grassland and indigenous forest, while the areas under cultivated land, commercial forestry and urban LULC classes increased. Future LULC projections showed that urban and agriculture land-uses increased significantly, with natural land-use classes such as grassland, other vegetation and indigenous forest declining in spatial extent across both. With an understanding of the extent of projected LULCC by 2030 within both catchments, proactive planning and management within the framework of sustainable land-use planning and water resource management in the respective catchments can be undertaken. Moreover, the results of the land-use modelling study can be used to infill historical data gaps, support effective land-use planning and provide a means to evaluate the impacts of different future development pathways.

FLUS Based Modeling of the Urban LULC in Arid and Semi-Arid Region of Northwest China: A Case Study of Urumqi City

Modeling land use and land cover (LULC) change is important for understanding its spatiotemporal trends and plays a crucial role in land use planning and natural resources management. To this end, this study assessed the spatiotemporal characteristics of the LULC changes in Urumqi city between 1980 and 2020. In addition, future LULC was successfully projected for 2030 and 2050 under different scenarios based on the FLUS model. This model was validated using actual and simulated land use data for 2020. The kappa coefficient and figure of merit of the simulation results for 2020 were 0.87 and 0.114, respectively, indicating that the simulation accuracy was satisfactory. The results demonstrated that grassland was the major land use type, with the area accounting for more than 50% of the study area. From 1980 to 2020, urban land greatly expanded, while grassland decreased significantly. Urban land increased from 353.51 km2 to 884.27 km2, while grassland decreased from 7903.4 km2 to 7414.92 km2 from 1980 to 2020. In addition, significant transitions mainly occurred between grasslands, cultivated lands and urban lands. Grassland and cultivated land were converted into urban land, resulting in rapid urban expansion over the last 40 years. From 1990 to 2000, grassland was converted into urban land with an area of 341.08 km2. Finally, the simulation results of the LULC showed that urban land is expected to increase under all three scenarios, and cultivated land, grassland and forest land are effectively protected under the Cultivated Land Protection Scenario (CPS) and Ecological Protection Scenario (EPS) compared to the Baseline Scenario (BLS). This study assessed the spatiotemporal characteristics and transitions of LULC between 1980 and 2020, and successfully projected LULC for 2035 and 2050 in Urumqi City in the arid and semi-arid regions of northwest China based on the FLUS model, which has not been investigated in previous studies.

Urban land-use classification using machine learning classifiers: comparative evaluation and post-classification multi-feature fusion approach

ABSTRACT Accurate spatial-temporal mapping of urban land-use and land-cover (LULC) provides critical information for planning and management of urban environments. While several studies have investigated the significance of machine learning classifiers for urban land-use mapping, the determination of the optimal classifiers for the extraction of specific urban LULC classes in time and space is still a challenge especially for multitemporal and multisensor data sets. This study presents the results of urban LULC classification using decision tree-based classifiers comprising of gradient tree boosting (GTB), random forest (RF), in comparison with support vector machine (SVM) and multilayer perceptron neural networks (MLP-ANN). Using Landsat data from 1984 to 2020 at 5-year intervals for the Greater Gaborone Planning Area (GGPA) in Botswana, RF was the best classifier with overall average accuracy of 92.8%, MLP-ANN (91.2%), SVM (90.9%) and GTB (87.8%). To improve on the urban LULC mapping, the study presents a post-classification multiclass fusion of the best classifier results based on the principle of feature in-feature out (FEI-FEO) under mutual exclusivity boundary conditions. Through classifier ensemble, the FEI-FEO approach improved the overall LULC classification accuracy by more than 2% demonstrating the advantage of post-classification fusion in urban land-use mapping.

Framework and Use Case for a Web-Based Interactive Analysis Tool to Investigate Urban Expansion and Sustainable Development Goal Indicators

Land cover changes have been mapped for decades to investigate urban expansion patterns. Under the UN Sustainable Development Goals (SDGs), several indices are employed to interpret urban growth trends quantitatively and comprehensively. However, landowners and the interested public usually have limited insights into these types of information as access to data and software is limited. Static maps and the inability to access special file formats increase the difficulty of viewing and investigating the data. This contribution presents a dedicated, interactive, web-based analysis tool for integrating land cover and land use maps as well as urban expansion indices. The tool’s concept, development and functionality are presented, and its general design is reviewed based on an actual implementation case. The setup allows integrating land use and land cover (LULC) change data alongside SDG indicators. The tool’s design aims to enhance user accessibility to information on urban expansion indices and LULC. We demonstrate that such a tool can be used to help disseminate results and to improve communication with the public in the context of other use cases.

COMPARISON OF MACHINE LEARNING CLASSIFIERS FOR MULTITEMPORAL AND MULTISENSOR MAPPING OF URBAN LULC FEATURES

Abstract. This study compares four machine-learning algorithms comprising of Classification And Regression Trees (CART), Random Forest (RF), Gradient Tree Boosting (GTB) and Support Vector Machine (SVM) for the classification of urban land-use and land-cover (LULC) features. Using multitemporal and multisensor Landsat data from 1984-2020 at 5-year intervals for the Greater Gaborone Planning Area (GGPA) in Botswana, the aim of the study is to determine the performance of the classifiers in the extraction of different urban LULC features as built-up, bare-soil, water, grass, shrubs and forest. The results show that for mapping built-up areas, RF and SVM presented the best results with overall accuracy of 85%. Bare soil is best mapped using RF and CART with accuracy of up to 98%, while SVM and GTB were most suitable for mapping water bodies. The suitable classifiers for mapping the vegetation classes were RF for grass (94.5%), SVM for shrubland (81.5%) and GTB for forest (84.3%). In terms of class specific accuracy, RF achieved the highest performance with average overall accuracy (OA) of 95.9%, SVM (95.8%), GTB (95.6%) and CART (95.1%). The same performance pattern was observed from the F1-score, True Positive Rate (TPR), False Positive Rate (FPR) and Area under ROC curve (AUC) metrices for the class classification accuracies. The overall accuracy for the eight-epoch years were RF (87.8%), SVM (87.5%), GTB (86.4%) and CART (85.3%). To improve on the urban LULC mapping, the study proposes the post-classification feature fusion of the best classifier results.

Variation in Temperature, Precipitation, and Vegetation Greenness Drive Changes in Seasonal Variation of Avian Diversity in an Urban Desert Landscape

Previous studies in urban desert ecosystems have reported a decline in avian diversity. Herein, we expand and improve these studies by disentangling the effect of land-use and land-cover (LULC) types (desert, riparian desert, urban, riparian urban, agriculture), vegetation greenness (normalized difference vegetation index—NDVI), climate, and their interactions on avian seasonal variation abundance and richness. Avian community data were collected seasonally (winter and spring) from 2001 to 2016. We used generalized linear mixed models (GLMM) and multimodel inference to investigate how environmental predictors explain patterns of avian richness and abundance. Avian abundance and richness oscillated considerably among the years. GLMM indicated that LULC was the most important predictor of avian abundance and richness. Avian abundance was highest in urban riparian and urban LULC types, followed by agriculture. In contrast, avian richness was the highest in riparian environments (urban and desert), followed by agriculture, urban, and desert. NDVI was also strongly related to avian abundance and richness, whereas the effect of temperature and precipitation was moderate. The importance of environmental predictors is, however, dependent on LULC. The importance of LULC, vegetation cover, and climate in influencing the seasonal patterns of avian distribution highlights birds’ sensitivity to changes in land use and cover and temperature.

Land–Use and Land-Cover Change Detection Using Dynamic Time Warping–Based Time Series Clustering Method

Accurate and timely monitoring of urban land-use and land-cover (LULC) change is useful for understanding the various impacts of human activity on the urban environment. In order to demonstrate the advantage of time series imaging for urban LULC change detection, we selected time series Landsat images over a two-year period to detect inter-annual changes. A time series trajectory for each pixel was developed by the biophysical composition index (BCI) and normalized vegetation index (NDVI) values extracted from Landsat images. Considering that temporal length of trajectories in different years might be different, the dynamic time warping (DTW) measure was selected as the LULC change magnitude indicator. After DTW-based change/unchanged detection, the DTW-based clustering method was used in LULC change type extraction. Finally, the overall accuracy of change/unchanged detection result was 92.3%, and the overall accuracy of all change types was 71%. Some change types that are difficult to extract by bi-temporal images were detected, such as inter-class changes between farmland and forest, and intra-class change of farmland, indicating the advantage of time series information in LULC change detection.

Urban Land Use and Land Cover Change Prediction via Self-Adaptive Cellular Based Deep Learning With Multisourced Data

The urban sustainable development becomes an essential goal presented in “2030 Agenda for Sustainable development” by the United Nations. Urban land use and land cover (LULC) change prediction, which is a significant indicator to evaluate urban construction strategy, urges to be solved. Remote sensing technique and neural network, are two practical tools widely utilized in urban LULC change prediction. Because of the rapid improvement of remote sensing technique, urban data can be periodically captured in a short time interval. A large amount of data cause the conventional neural networks method having bad efficiency in dealing with it. Moreover, as human activities are in high intensity in the urban area, society related factors require to be taken into consideration in urban land change trend prediction. In this article, a self-adaptive cellular-based deep learning analysis method by utilizing the multisourced data is proposed for urban LULC change prediction. Multisourced data, including weather related data, economy related data, construction related data, and remote sensing data, are normalized and formalized by the proposed self-adaptive cellular-based method. Deep learning long short term memory neural network, which is an advanced model of recurrent neural network and has a strong ability in dealing with sequence data, is utilized to urban LULC change prediction. Remote sensing data captured from 1984 to 2016 are utilized to conduct experiments. Experiment results illustrate that the proposed method can effectively and efficiently make LULC change prediction, and the accuracy is up to $\text{93.1}\%$ .

Urban Land Use and Land Cover Classification Using Multisource Remote Sensing Images and Social Media Data

Land use and land cover (LULC) are diverse and complex in urban areas. Remotely sensed images are commonly used for land cover classification but hardly identifies urban land use and functional areas because of the semantic gap (i.e., different definitions of similar or identical buildings). Social media data, “marks” left by people using mobile phones, have great potential to overcome this semantic gap. Multisource remote sensing data are also expected to be useful in distinguishing different LULC types. This study examined the capability of combined multisource remote sensing images and social media data in urban LULC classification. Multisource remote sensing images included a Chinese ZiYuan-3 (ZY-3) high-resolution image, a Landsat 8 Operational Land Imager (OLI) multispectral image, and a Sentinel-1A synthetic aperture radar (SAR) image. Social media data consisted of the hourly spatial distribution of WeChat users, which is a ubiquitous messaging and payment platform in China. LULC was classified into 10 types, namely, vegetation, bare land, road, water, urban village, greenhouses, residential, commercial, industrial, and educational buildings. A method that integrates object-based image analysis, decision trees, and random forests was used for LULC classification. The overall accuracy and kappa value attained by the combination of multisource remote sensing images and WeChat data were 87.55% and 0.84, respectively. They further improved to 91.55% and 0.89, respectively, by integrating the textural and spatial features extracted from the ZY-3 image. The ZY-3 high-resolution image was essential for urban LULC classification because it is necessary for the accurate delineation of land parcels. The addition of Landsat 8 OLI, Sentinel-1A SAR, or WeChat data also made an irreplaceable contribution to the classification of different LULC types. The Landsat 8 OLI image helped distinguish between the urban village, residential buildings, commercial buildings, and roads, while the Sentinel-1A SAR data reduced the confusion between commercial buildings, greenhouses, and water. Rendering the spatial and temporal dynamics of population density, the WeChat data improved the classification accuracies of an urban village, greenhouses, and commercial buildings.

Dynamics of Land Use and Land Cover Changes in Harare, Zimbabwe: A Case Study on the Linkage between Drivers and the Axis of Urban Expansion

With increasing population growth, the Harare Metropolitan Province has experienced accelerated land use and land cover (LULC) changes, influencing the city’s growth. This study aims to assess spatiotemporal urban LULC changes, the axis, and patterns of growth as well as drivers influencing urban growth over the past three decades in the Harare Metropolitan Province. The analysis was based on remotely sensed Landsat Thematic Mapper and Operational Land Imager data from 1984–2018, GIS application, and binary logistic regression. Supervised image classification using support vector machines was performed on Landsat 5 TM and Landsat 8 OLI data combined with the soil adjusted vegetation index, enhanced built-up and bareness index and modified difference water index. Statistical modelling was performed using binary logistic regression to identify the influence of the slope and the distance proximity characters as independent variables on urban growth. The overall mapping accuracy for all time periods was over 85%. Built-up areas extended from 279.5 km2 (1984) to 445 km2 (2018) with high-density residential areas growing dramatically from 51.2 km2 (1984) to 218.4 km2 (2018). The results suggest that urban growth was influenced mainly by the presence and density of road networks.

Monitoring and quantification of stormwater runoff from mixed land use and land cover catchment in response to land development

Understanding the influence of land use and land cover (LULC) change in stormwater runoff is important for watershed management. In this study, integration of 31 storm events, monthly monitoring of LULC change, Pearson’s correlation, multiple linear regression analysis (MLR) and Personalized Computer Storm Water Management Model (PCSWMM) were applied to quantify the influence of LULC change on stormwater quality from mixed LULC catchment with ongoing land development in Yongin, South Korea. Due to ongoing land development in the catchment, bare land and urban LULC were exponentially increased while agriculture, forest, grassland and water LULC decreased in spatial extent. The correlation analysis showed that stormwater quality was positively correlated to bare land (0.595; Cl – 0.891; TSS, p < 0.05) and urban (0.768; TN – 0.987; TSS, p < 0.05); negatively correlated to forest (−0.593; Cu – 0.532; BOD5, p < 0.05) and grassland (−0.587; TSS – 0.512; BOD5, p < 0.05) and; either positively or no correlation to agriculture (0.064; Cu – 0.871; TSS, p < 0.05) and water (−0.131; Cl – 0.221; TP, p < 0.05). Furthermore, the MLR analysis showed that combinations of different LULC were able to describe the overall stormwater quality of the catchment. Moreover, the LULC scenario analysis demonstrate that under dominant agriculture (S1), bare land (S2) and urban areas (S5), the average pollutant concentrations would increase by as much as 13.22% (Cl; S2; pre-) to 59.25% (TSS; S5; early-active); while under dominant forest (S3) and grassland (S4) the average pollutant concentration would decrease by as much as −53% (Pb; S3; late-active) to −3.22% (BOD5; S4; pre-). These findings explained that the variability of pollutant concentrations in different phase of land development was affected by expansion of bare land and urban spatial extent, increase of hydrological characteristics (total rainfall and average rainfall intensity) and massive soil activities (soil digging and soil transfer). Therefore, results of this study will provide scientific information to establish a cost-effective stormwater management, development of empirical model, and designing monitoring strategies and guidelines to minimize the negative impact of LULC change on stormwater runoff.

Mapping urban impervious surface using object-based image analysis with WorldView-3 satellite imagery

Land use and land cover (LULC) data are important to monitor and assess environmental change. LULC classification using satellite images is a method widely used on a global and local scale. Especially, urban areas that have various LULC types are important components of the urban landscape and ecosystem. This study aims to classify urban LULC using WorldView-3 (WV-3) very high-spatial resolution satellite imagery and the object-based image analysis method. A decision rules set was applied to classify the WV-3 images in Kathu subdistrict, Phuket province, Thailand. The main steps were as follows: (1) the image was ortho-rectified with ground control points and using the digital elevation model, (2) multiscale image segmentation was applied to divide the image pixel level into image object level, (3) development of the decision ruleset for LULC classification using spectral bands, spectral indices, spatial and contextual information, and (4) accuracy assessment was computed using testing data, which sampled by statistical random sampling. The results show that seven LULC classes (water, vegetation, open space, road, residential, building, and bare soil) were successfully classified with overall classification accuracy of 94.14% and a kappa coefficient of 92.91%.

LULC analysis of urban spaces using Markov chain predictive model at Ranchi in India

Monitoring of land use and land cover (LULC) change is one important drivers of global change, which plays a decisive role on the management and sustainable developmental planning for urban spaces. The study aims to develop series of LULC maps of urban areas of Ranchi, India and was studied during the years 1989 and 2015. It predicts LULC changes using geospatial tools such as remote sensing and GIS. Various satellite imagery datasets such as Landsat TM, ETM+ and Landsat 8 OLI of years 1989, 2002 and 2015 were used to analyze urban LULC, which was later used to predict for 2015 and 2028 using Markov transition matrix and was cross-validated with true LULC of 2015. The urban area growth was found 11% more than the predicted value. Slope map was also generated from digital elevation model and urban expansion in 2015 was 67% and with respect to roads it was 60% within 1 km road buffer in 2015 over 2002. Regression equation was developed over decadal population of 1961–2011 to estimate it for years 1989, 2002, 2015 and 2028. The population has increased 102% in 2015 over 1989. However, Markov predicted 43% more urban expansion for year 2028 over 2015. Coarse resolution temporal satellite data can be effectively harnessed to assess LULC change whereas prediction can be done with accuracy as high as 89.02% based on Markov transition matrix. An effective coordination between governments agencies are solicited to achieve sustainable development to be implemented systematically.

Monitoring and predicting the fecal indicator bacteria concentrations from agricultural, mixed land use and urban stormwater runoff

While the urban runoff are increasingly being studied as a source of fecal indicator bacteria (FIB), less is known about the occurrence of FIB in watershed with mixed land use and ongoing land use and land cover (LULC) change. In this study, Escherichia coli (EC) and fecal streptococcus (FS) were monitored from 2012 to 2013 in agricultural, mixed and urban LULC and analyzed according to the most probable number (MPN). Pearson correlation was used to determine the relationship between FIB and environmental parameters (physicochemical and hydrometeorological). Multiple linear regressions (MLR) were used to identify the significant parameters that affect the FIB concentrations and to predict the response of FIB in LULC change. Overall, the FIB concentrations were higher in urban LULC (EC=3.33–7.39; FS=3.30–7.36log10MPN/100mL) possibly because of runoff from commercial market and 100% impervious cover (IC). Also, during early-summer season; this reflects a greater persistence and growth rate of FIB in a warmer environment. During intra-event, however, the FIB concentrations varied according to site condition. Anthropogenic activities and IC influenced the correlation between the FIB concentrations and environmental parameters. Stormwater temperature (TEMP), turbidity, and TSS positively correlated with the FIB concentrations (p>0.01), since IC increased, implying an accumulation of bacterial sources in urban activities. TEMP, BOD5, turbidity, TSS, and antecedent dry days (ADD) were the most significant explanatory variables for FIB as determined in MLR, possibly because they promoted the FIB growth and survival. The model confirmed the FIB concentrations: EC (R2=0.71–0.85; NSE=0.72–0.86) and FS (R2=0.65–0.83; NSE=0.66–0.84) are predicted to increase due to urbanization. Therefore, these findings will help in stormwater monitoring strategies, designing the best management practice for FIB removal and as input data for stormwater models.

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

Abstract The relationship between land use and land cover (LULC) patterns and thermal characteristic has long been studied for examining the impact of urbanization on urban thermal environment. Previous studies were inclined to the use of one or a few images without fully considering the temporal domain of the reflective and thermal infrared data on board Landsat sensors. This paper took the Atlanta metropolitan area as a case study to illustrate LULC change and its impact on land surface temperature (LST) variations. The Landsat L1T (Standard terrain correction) images from TM/ETM + from 1984 to 2011 (507 in total) were downloaded through the USGS online portal and consistently calibrated to surface reflectance and brightness temperature (BT). The cloud-, cloud shadow-, and snow-contaminated pixels were excluded in the analysis according to the metadata, and a further screening procedure based on the RIRLS (Robust Iteratively Reweighted Least Squares) technique was implemented. The time series LSTs (TSLSTs) was derived using the single channel algorithm because it required only the parameters of water vapor and land surface emissivity and had a reported error close to 1 K. The LULC classification and change detection was accomplished by using the Continuous Change Detection and Classification (CCDC) algorithm. The TSLSTs were further decomposed into the seasonal and trend components by an additive model. Results showed that the overall LULC classification and change detection accuracies were 89% and 92%, respectively. Urban growth was mainly observed in Fulton County and Gwinnett County. High-intensity urban land had the largest mean LST value of 294.9 K and yearly amplitude of 17.4 K. A comparison of the trend component between urban and non-urban land covers showed a difference of 1.8 K per decade. Next, temporal thermal signatures were created to characterize and quantify the impact of urban LULC changes. Decomposition analysis showed that the conversion of evergreen forest to medium-intensity urban land generated the largest difference in annual LST variation (5.7 K) and the largest trend difference (0.0004 K/day).