Normalized Difference Built-up Index Research Articles

Analysis on Thermal Islands Effectors in Ramadi City, Iraq Using Multi-temporal Landsat Images

This paper analyzes the effects of urban density, vegetation cover, and water body on thermal islands measured by land surface temperature in Al Anbar province, Iraq using multi-temporal Landsat images. Images from Landsat 7 ETM and Landsat 8 OLI for the years 2000, 2014, and 2018 were collected, pre-processed, and anal yzed. The results suggested that the strongest correlation was found between the Normalized Difference Built-up Index (NDBI) and the surface temperature. The correlation between the Normalized Difference Vegetation Index (NDVI) and the surface temperature was slightly weaker compared to that of NDBI. However, the weakest correlation was found between the Normalized Difference Water Index (NDWI) and the temperature. The results obtained in this research may help the decision makers to take actions to reduce the effects of thermal islands by looking at the details in the produced maps and the analyzed values of these spectral indices.

Exploring the Dynamics of Urban Greenness Space and Their Driving Factors Using Geographically Weighted Regression: A Case Study in Wuhan Metropolis, China

Urban greenness plays a vital role in supporting the ecosystem services of a city. Exploring the dynamics of urban greenness space and their driving forces can provide valuable information for making solid urban planning policies. This study aims to investigate the dynamics of urban greenness space patterns through landscape indices and to apply geographically weighted regression (GWR) to map the spatially varied impact on the indices from economic and environmental factors. Two typical landscape indices, i.e., percentage of landscape (PLAND) and aggregation index (AI), which measure the abundance and fragmentation of urban greenness coverage, respectively, were taken to map the changes in urban greenness. As a case study, the metropolis of Wuhan, China was selected, where time-series of urban greenness space were extracted at an annual step from the Landsat collections from Google Earth Engine during 2000–2018. The study shows that the urban greenness space not only decreased significantly, but also tended to be more fragmented over the years. Road network density, normalized difference built-up index (NDBI), terrain elevation and slope, and precipitation were found to significantly correlate to the landscape indices. GWR modeling successfully captures the spatially varied impact from the considered factors and the results from GWR modeling provide a critical reference for making location-specific urban planning.

Using Machine Learning Algorithms to Estimate Soil Organic Carbon Variability with Environmental Variables and Soil Nutrient Indicators in an Alluvial Soil

Soil organic carbon (SOC) is an important indicator of soil quality and directly determines soil fertility. Hence, understanding its spatial distribution and controlling factors is necessary for efficient and sustainable soil nutrient management. In this study, machine learning algorithms including artificial neural network (ANN), support vector machine (SVM), cubist regression, random forests (RF), and multiple linear regression (MLR) were chosen for advancing the prediction of SOC. A total of sixty (n = 60) soil samples were collected within the research area at 30 cm soil depth and measured for SOC content using the Walkley–Black method. From these samples, 80% were used for model training and 21 auxiliary data were included as predictors. The predictors include effective cation exchange capacity (ECEC), base saturation (BS), calcium to magnesium ratio (Ca_Mg), potassium to magnesium ratio (K_Mg), potassium to calcium ratio (K_Ca), elevation, plan curvature, total catchment area, channel network base level, topographic wetness index, clay index, iron index, normalized difference build-up index (NDBI), ratio vegetation index (RVI), soil adjusted vegetation index (SAVI), normalized difference vegetation index (NDVI), normalized difference moisture index (NDMI) and land surface temperature (LST). Mean absolute error (MAE), root-mean-square error (RMSE) and R2 were used to determine the model performance. The result showed the mean SOC to be 1.62% with a coefficient of variation (CV) of 47%. The best performing model was RF (R2 = 0.68) followed by the cubist model (R2 = 0.51), SVM (R2 = 0.36), ANN (R2 = 0.36) and MLR (R2 = 0.17). The soil nutrient indicators, topographic wetness index and total catchment area were considered an indicator for spatial prediction of SOC in flat homogenous topography. Future studies should include other auxiliary predictors (e.g., soil physical and chemical properties, and lithological data) as well as cover a broader range of soil types to improve model performance.

Multi-Temporal Land Cover Change Mapping Using Google Earth Engine and Ensemble Learning Methods

The study deals with the application of Google Earth Engine (GEE), Landsat data and ensemble-learning methods (ELMs) to map land cover (LC) change over a decade in the Kaski district of Nepal. As Nepal has experienced extensive changes due to natural and anthropogenic activities, monitoring such changes are crucial for understanding relationships and interactions between social and natural phenomena and to promote better decision-making. The main novelty lies in applying the XGBoost classifier for LC mapping over Nepal and monitoring the decadal changes of LC using ELMs. To map the LC change, a yearly cloud-free composite Landsat image was selected for the year 2010 and 2020. Combining the annual normalized difference vegetation index, normalized difference built-up index and modified normalized difference water index, with elevation and slope data from shuttle radar topography mission, supervised classification was performed using a random forest and extreme gradient boosting ELMs. Post classification change detection, validation and accuracy assessment were executed after the preparation of the LC maps. Three evaluation indices, namely overall accuracy (OA), Kappa coefficient, and F1 score from confusion matrix reports, were calculated for all the points used for validation purposes. We have obtained an OA of 0.8792 and 0.875 for RF and 0.8926 and 0.8603 for XGBoost at the 95% confidence level for 2010 and 2020 LC maps, which are better for mountainous terrain. The applied methodology could be significant in utilizing the big earth observation data and overcoming the traditional computational challenges using GEE. In addition, the quantification of changes over time would be helpful for decision-makers to understand current environmental dynamics in the study area.

Exploring the Relationship Between Land Surface Temperature and Land Use Change in Lahore Using Landsat Data


 The present study focuses on determining the correlation of land surface temperature (LST) with normalized difference builtup index (NDBI) and normalized difference vegetation index (NDVI) of Lahore, a metropolitan city of Pakistan using landsat 5 and 8 dataset. This study also categorizes different types of land use through supervised image classification scheme and maximum likelihood algorithm (MLA), and assess the correlation between LST and land use type of different classes. The results of the study indicate that modifications in type of land use altered spatial variations of land surface temperature in 1990 and 2015. The findings also show that the ever increasing temperature caused by impervious surfaces such as builtup area, roads, construction sites and vacant land considerably contributes to heat island effect. However, vegetation cover, green and blue spaces decrease LST and effectively relieve the effect of heat island. LST builds a strong positive correlation with NDBI and strong negative correlation with NDVI. Based on the regression analysis between LST and NDBI and NDVI, these indices can be utilized as a sign to assess the impact of LU changes on temperature. The results further indicates that LST changes follow the pattern of LU changes in Lahore and the warmness intensity has been observed highest in the high density builtup area and vacant land, while low at the green and blue spaces. The analysis reveals that an increase in LST by 1.98 °C during the period of 25 years at the rate of 0.079 °C/year in high density builtup area was due to the excessive increase in settlement growth. The study concludes that change of land use has an effect on the LST in Lahore.
 

Developing a novel proximity analysis approach for assessment of waste management cost efficiency in low population density regions

Waste management system efficiency can be challenging to assess in regions with low population density or where data is only available at certain spatial resolutions. A novel spatial analysis approach is developed and applied in Nova Scotia, Canada to assess costs related to waste collection. Standard Deviational Ellipses are used to narrow areas of interest based on 14 administrative regions. Using these defined areas of interest, land (remote sensing) and infrastructure (vector) datasets are gathered. Pearson correlation is used to explore the relationships between the features and collection cost indicators. Two original indices are developed and analyzed: dwelling ratio (area of dwellings/total building area) and diversion-collection expenditure ratio. Results suggest that normalized difference vegetation index was positively related to collection cost while normalized difference built-up index was positively related to diversion indicators. Waste management facility distribution and road network significantly impact collection costs, and they are important in effective and efficient waste management system. Industrial indicators were also analyzed, and the number of utility towers was found to be related to collection costs. These findings suggest that geospatially dependent policies could be used to develop sustainable and economically efficient waste management systems in Nova Scotia and other low population density areas.

Combination of Landsat 8 OLI and Sentinel-1 SAR Time-Series Data for Mapping Paddy Fields in Parts of West and Central Java Provinces, Indonesia

The rise of Google Earth Engine, a cloud computing platform for spatial data, has unlocked seamless integration for multi-sensor and multi-temporal analysis, which is useful for the identification of land-cover classes based on their temporal characteristics. Our study aims to employ temporal patterns from monthly-median Sentinel-1 (S1) C-band synthetic aperture radar data and cloud-filled monthly spectral indices, i.e., Normalized Difference Vegetation Index (NDVI), Modified Normalized Difference Water Index (MNDWI), and Normalized Difference Built-up Index (NDBI), from Landsat 8 (L8) OLI for mapping rice cropland areas in the northern part of Central Java Province, Indonesia. The harmonic function was used to fill the cloud and cloud-masked values in the spectral indices from Landsat 8 data, and smile Random Forests (RF) and Classification And Regression Trees (CART) algorithms were used to map rice cropland areas using a combination of monthly S1 and monthly harmonic L8 spectral indices. An additional terrain variable, Terrain Roughness Index (TRI) from the SRTM dataset, was also included in the analysis. Our results demonstrated that RF models with 50 (RF50) and 80 (RF80) trees yielded better accuracy for mapping the extent of paddy fields, with user accuracies of 85.65% (RF50) and 85.75% (RF80), and producer accuracies of 91.63% (RF80) and 93.48% (RF50) (overall accuracies of 92.10% (RF80) and 92.47% (RF50)), respectively, while CART yielded a user accuracy of only 84.83% and a producer accuracy of 80.86%. The model variable importance in both RF50 and RF80 models showed that vertical transmit and horizontal receive (VH) polarization and harmonic-fitted NDVI were identified as the top five important variables, and the variables representing February, April, June, and December contributed more to the RF model. The detection of VH and NDVI as the top variables which contributed up to 51% of the Random Forest model indicated the importance of the multi-sensor combination for the identification of paddy fields.

Performance testing of selected spectral indices in automated extraction of impervious built-up surface features using Resourcesat LISS-III image

The increasing urban density and spatial expansion of urbanized areas result in changes from the natural landscape to impervious surface features. Remote sensing–based spectral indices provide an efficient method in the automated identification of land use and cover classes. However, a common challenge is the accurate extraction of built-up features from satellite images. The original Normalized Difference Built-up Index (NDBI) has been modified by several researchers in the anticipation of improvement of the built-up area classification. The indices adopted in the study are Index-based Built-up Index (IBI), Built Up Index (BUI), NDBI, Modified Built-up Index (MBI), and the newly developed Impervious Built-up Index (IBUI). These indices work on automated kernel-based probabilistic thresholding algorithm to group the index values into built-up and non-built-up areas. This study investigates the performance of the above mentioned five spectral indices on Resourcesat LISS III imageries over the Kolkata-Howrah urban agglomeration, India. The indices were compared and found based on the overall accuracy that the novel IBUI provided the maximum accuracy (94%) in extracting impervious built-up areas. Based on the overall accuracy and kappa statistic the IBUI was chosen for estimating spatio-temporal urban growth of the study area from 2003 to 2018 based on the same LISS-III sensor data. It was calculated that the urban built-up area has increased from 144 to 177 km2.

RUESVMs: An Ensemble Method to Handle the Class Imbalance Problem in Land Cover Mapping Using Google Earth Engine

Timely and accurate Land Cover (LC) information is required for various applications, such as climate change analysis and sustainable development. Although machine learning algorithms are most likely successful in LC mapping tasks, the class imbalance problem is known as a common challenge in this regard. This problem occurs during the training phase and reduces classification accuracy for infrequent and rare LC classes. To address this issue, this study proposes a new method by integrating random under-sampling of majority classes and an ensemble of Support Vector Machines, namely Random Under-sampling Ensemble of Support Vector Machines (RUESVMs). The performance of RUESVMs for LC classification was evaluated in Google Earth Engine (GEE) over two different case studies using Sentinel-2 time-series data and five well-known spectral indices, including the Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Soil-Adjusted Vegetation Index (SAVI), Normalized Difference Built-up Index (NDBI), and Normalized Difference Water Index (NDWI). The performance of RUESVMs was also compared with the traditional SVM and combination of SVM with three benchmark data balancing techniques namely the Random Over-Sampling (ROS), Random Under-Sampling (RUS), and Synthetic Minority Over-sampling Technique (SMOTE). It was observed that the proposed method considerably improved the accuracy of LC classification, especially for the minority classes. After adopting RUESVMs, the overall accuracy of the generated LC map increased by approximately 4.95 percentage points, and this amount for the geometric mean of producer’s accuracies was almost 3.75 percentage points, in comparison to the most accurate data balancing method (i.e., SVM-SMOTE). Regarding the geometric mean of users’ accuracies, RUESVMs also outperformed the SVM-SMOTE method with an average increase of 6.45 percentage points.

Landslide Susceptibility Prediction Based on the Information Value-Logistic Regression Model and Geographic Information System

The landslide susceptibility prediction (LSP) is generally implemented using certain types of single models; however, certain drawbacks exist in the single models; e.g., it is difficult to clearly reflect the weights of landslide-related environmental factors if only the information value (IV) model is adopted. To overcome these limitations, this study proposes an IV-logistic regression (IV-LR) model for LSP. The landslides that occurred in the southern part of Chongyi County, China, are used as study cases. Nine environmental factors—elevation, slope, plane curvature, profile curvature, relief amplitude, distance to river, lithology, normalized difference vegetable index, and normalized difference built-up index—are adopted based on remote sensing and geographic information system. Certain landslide grid units and the same number of non-landslide grid units are used as the output variables of these models. The IV, LR, and IV-LR models are used to implement the LSP in the southern part of Chongyi County. The predicted landslides susceptibility in Chongyi County mostly occurred in areas with low elevations, close distance to rivers, carbonate lithology, low vegetation coverage rate, and densely populated areas. The results show that the prediction rate of the IV-LR model (80.4%) is higher than that of the LR model (76.8%), followed by the IV model (72.8%); they further demonstrate that the IV-LR model has its unique superiority and rationality compared with the IV and LR models.

Extracting urban impervious surfaces from Sentinel-2 and Landsat-8 satellite data for urban planning and environmental management.

Impervious surface is mainly defined as any surface which water cannot infiltrate the soil. Due to the impact of urban impervious surfaces (UIS) on environmental issues, the amount of impervious surfaces has been recognized as the most significant index of environmental quality. Detection and analysis of impervious surfaces within a watershed is one of the developing areas of scientific interest. This study evaluates and compares the accuracy and performance of five classification algorithms-supervised object-based nearest neighbour (NN) classifier, supervised pixel-based maximum likelihood classifier (MLC), supervised pixel-based spectral angle mapper (SAM), band ratioing normalized difference built-up index (NDBI), and normalized difference impervious index (NDII)-in extracting urban impervious surfaces. Our first aim was to identify the most effective method for mapping UIS using Sentinel-2A and Landsat-8 satellite data. The second aim was to compare and reveal the efficiency of the spatial and spectral resolution of Sentinel-2A and Landsat-8 data in extracting UIS. The results revealed that the supervised object-based NN approach using the visible and near-infrared bands of both satellite imagery produced the most homogenous and accurate map among the other methods. The object-based NN algorithm achieved an overall classification accuracy of 90.91% and 88.64%, and Kappa coefficient of 0.82 and 0.77 for Sentinel-2 and Landsat-8 images, respectively. The study also showed that the Sentinel-2 image yielded better results than the Landsat-8 pan-sharpened image in extracting detail and classification accuracy. Comparing these methods in the selected challenging study area can provide insight into the selection of the classification method for rapid and reliable extraction of UIS.

SURFACE TEMPERATURE DYNAMICS IN RESPONSE TO LAND COVER TRANSFORMATION

This research assessed the micro-level Land Surface Temperature (LST) dynamics in response to Land Cover Type Transformation (LCTT) at Khulna City Corporation Ward No 9, 14, 16 from 2001 to 2019, through raster-based analysis in geo-spatial environment. Satellite images (Landsat 5 TM and Landsat 8 OLI) were utilized to analyze the LCTT and its influences on LST change. Different indices like Normalized Difference Moisture Index (NDMI), Normalized Difference Vegetation Index (NDVI), Normalized Difference Buildup Index (NDBI) were adopted to show the relationship against the LST dynamics individually. Most likelihood supervised image classification and land cover change direction analysis shows that about 27.17%, 17.83% and 4.73% buildup area has increased at Ward No 9, 14, 16 correspondingly. On the other hand, the distribution of change in average LST shows that water, vacant land, and buildup area recorded the highest increase in temperature by 2.720C, 4.150C, 4.590C, respectively. The result shows the average LST increased from 25.800C to 27.150C in Ward No 9, 26.840C to 27.230C in Ward No 14 and 26.870C to 27.120C in Ward No 16. Here, the most responsible factor is the transformation of land cover in buildup areas.

Possibility for Identifying/Extracting Rock Outcrop Using Landsat 8 OLI/TIRS - Case Study of Thua Thien Hue Province

This paper approaches the ratio image method to extract the exposed rock information from the Landsat 8 OLI/TIRS satellite image (2019) according to the object orientation classification. Combining automatic interpretation and interpretation through threshold of image index values according to interpretation key the object orientation classification to separate soil object containing exposed rock and no exposed rock in Thua Thien Hue province. Using the Topsoil Grain Size Index (TGSI), the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Built-up Index (NDBI) and other related analytical problems have identified 40 exposed rock storage areas in the study area. The results have been verified in the field and the Kappa index is 85.10%.

Spatiotemporal analysis of land surface temperature using multi-temporal and multi-sensor image fusion techniques

Urban Heat Island (UHI) is a major challenge in urban environments that affects human activities. This phenomenon is caused and changed by various factors in urban environments, in which identification of such parameters requires fine-scale satellite images. This study, therefore, aimed at investigating spatiotemporal changes of UHIs as well as identification of important factors using remote sensing image fusion techniques in Rasht. The image dataset comprises of Landsat 5, 7, 8 and MODIS images from 2001 to 2018. After pre-processing, multi-temporal and multi-sensor image fusion techniques were used to retrieve Landsat 7 images as well as Landsat-like medium spatial resolution images. The effects of built-up areas and surface biophysical characteristics such as brightness, greenness and wetness were also examined on LST changes through time. The results showed that multi-temporal and multi-sensor image fusion methods provide appropriate accuracies and the multi-temporal fusion method performs better than the multi-sensor fusion method. The results also showed that retrieval of sensor products is of higher accuracy than that of the spectral bands. The spatiotemporal changes of UHIs were indicative of an increasing trend over time. Of the surface biophysical parameters, the normalized difference built-up index exhibited the highest correlation with changes of normalized LST.

Effects of land use and land cover pattern on urban temperature variations: A case study in Hong Kong

A remarkable phenomenon in urban climate is the urban heat island (UHI) effect. Based on satellite imagery and geographical and climatological data, this study analyzes the patterns of land surface temperature (LST) and land use/land cover (LULC) in Hong Kong and the seasonal variations of the relationships between them. Remote sensing, Geographical Information System (GIS) and statistical methods are used for analysis. The results indicate that LST is significantly affected by LULC types. The ordinary least squared (OLS) models indicate that LST is positively related to Normalized Difference Built-up Index (NDBI) and Normalized Difference Bareness Index (NDBaI) but negatively related to Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), elevation and wind speed. The Geographically Weighted Regression (GWR) model indicates that LST is negatively related to NDVI and elevation. The magnitudes of the influences of indices vary with the season. The GWR model demonstrates better fitness compared to the OLS models. Regression results on the GIS maps reveal the fitness of the models across the study area and the spatial variations of the regression coefficients. Such information may help decision makers develop policies at the regional scale and fine-tune planning practices at the local scale to improve the urban thermal environment.

Impact of spatiotemporal land-use and land-cover changes on surface urban heat islands in a semiarid region using Landsat data

ABSTRACT Many factors are involved in urban heat island development, such as lack of green spaces, improper choice of building materials, densification, and other human activities. The aim of this research was to quantify the effects of land-use/land-cover (LU/LC) changes on urban land surface temperature (LST) during a 25-year period (1993–2018) for the semiarid Shiraz City in southern Iran using Landsat data (TM, ETM+, and OLI/TIRS) and machine learning algorithms. Five main LU/LC classes, such as orchard, vegetation, bare surface, asphalt cover, and built-up areas, were identified using a support vector machine algorithm. Landsat images were used to retrieve normalized difference vegetation index (NDVI) and normalized difference built-up index (NDBI). The results showed that the mean LST over the entire study domain increased considerably between 1993 and 2018, due to urbanization, decrease of green areas, and increasing industrial areas. Built-up areas increased considerably by 25.8% from 80 to 100.6 km2 between 1993 and 2018, while vegetation cover decreased dramatically by 69.3%. Mean LST increased from 38.4 to 40.2°C during the 25-year period with a significant increase of 3.9°C between 2013 and 2018. In addition, the Urban heat island Ratio Index (URI) displayed a substantial upward trend during the 25-year period.

Establishment and application of the modified Chen NDVI model integrated with ground object classification

Heterogeneous land surface causes the scale effect of remotely sensed land surface parameters. Addressing on quantitatively describing the influence of different ground objects on scale effect of the common surface parameter normalized difference vegetation index (NDVI), the paper proposed an improved NDVI scale transformation model. The model integrated accurate classification information from medium- or high- spatial resolution remote sensing images to improve the traditional Chen NDVI scale conversion model, and showed its superiority for NDVI scale effect description. Xiamen was taken as the experimental area for the study and the conclusions could be obtained from the experimental results. Compared with the traditional Chen NDVI model with rough information, the improved Chen NDVI model incorporating fine ground information provides a finer and more quantitative description of the influence of different land types on the NDVI scale effect. Furthermore, it is found that the presence of water is the key factor underlying the NDVI scale effect. The conclusions of this study have important implications for the scale effect research of other NDVI-like surface parameters such as ratio vegetation index (RVI), normalized difference built-up index (NDBI), normalized burn ratio (NBR).

A spatial-temporal analysis of urban growth in melbourne; Were local government areas moving toward compact or sprawl from 2001–2016?

In most cities, urban growth follows a sprawl pattern. In the Melbourne metropolitan area, the Plan Melbourne (2017–2050) was formulated with the aim of reducing sprawl and encouraging sustainable growth. However, the Internet research has shown that in the metropolitan area of Melbourne, no studies aiming to investigate the urban growth using satellite images and analyzing compact urban growth drivers (CUGDs) have been conducted. The objectives of this study of the Melbourne metropolitan area are as follows: 1) Analyzing spatial-temporal changes of normalized difference built-up index (NDBI) 2) Analyzing CUGDs 3) Evaluating the relationship between NDBI and CUGDs. 4) Determining the type of growth in metropolitan area divisions regarding NDBI and CUGDs (2001–2016). To calculate NDBI and urban built-up areas Landsat satellite images were used. Five indicators of population density, separate house density, apartment house density, public transportation usage ratio and distance from the city center were analyzed as CUGDs. The relationship between NDBI and CUGDs was assessed using Ordinary Least Squares (OLS). GIS and ENVI software packages were used for these analyses. The findings revealed that outer Melbourne has experienced the highest rate of change in built-up areas and the direction of developments are toward west, north and south-east in conformity with the government policies. The highest annual rate of change belongs to apartment house density. During 2001–2006, it was 67%, during 2006–2011 45%, during 2011–2016 58%, and during 2001–2016, 61% of the variation in NDBI was explained by the variables. Changes in NDBI under various CUGDs conditions led to the formation of patterns conceptualized at the level of thirty-one local governments (LGs). Based on this study's findings, practical strategies should be formulated to guide the future development of the city and to achieve the Plan Melbourne (2017–2050) objective.

MODELLING OF LAND SURFACE TEMPERATURE USING GRAY LEVEL CO-OCCURRENCE MATRIX AND RANDOM FOREST REGRESSION

Abstract. Modelling of land surface temperature (LST) is conducted to be able to explain the spatial and temporal variations of LST using a set of explanatory variables. LST in a previous study was modelled as a linear function of vegetation cover and built up cover as quantified by the normalized difference vegetation index (NDVI) and the normalized difference built-up index (NDBI), respectively, and other variables, namely, albedo, solar radiation (SR), surface area-volume ratio (SVR), and skyview factor (SVF). SVF requires a digital surface model of sufficient resolution while SVR computation needs 3D volumetric features representing buildings as input. These inputs are typically not readily available. In addition, NDVI and NDBI do not fully describe the spatial variability of vegetation and built-up cover within an LST pixel. In this study, PlanetScope images (3m resolution) were processed to provide soil-adjusted vegetation index (SAVI) and VgNIR Built-up Index (VgNIR-BI) layers. The following gray level co-occurrence matrices (GLCM) were generated from SAVI and VgNIR-BI: Mean, Variance, Homogeneity, Contrast, Dissimilarity, Entropy, Second Moment, and Correlation. Random Forest regression was run for several cases with different combinations of GLCM features and non-GLCM variables. Using GLCM features alone yielded less satisfactory models. However, the use of additional GLCM features in combination with other variables resulted in lower MSE and a slight increase in R2. Considering NDBI, NDVI, SAVI_GLCM_contrast, VgNIR-BI_GLCM_contrast, VgNIR-BI_GLCM_dissimilarity, and SAVI_GLCM_contrast only, the RF model yielded an MSE=1.657 and validation R2=0.822. While this 6-variable model’s performance is slightly less, the need for DSM and 3D building models which are necessary for the generation of SVF and SVR layers is eliminated. Exploratory regression (ER) was also conducted. The best 6-variable ER model (Adj. R2=0.79) consists of SVR, NDBI, NDVI, SAVI_GLCM_second_moment, VgNIR-BI_GLCM_mean, and VgNIR-BI_GLCM_entropy. In comparison, OLS regression using the 6 non-GLCM variables yielded an Adj. R2=0.691. The results of RFR and ER both indicate the value of GLCM features in providing valuable information to the models of LST. LST is best described through a combination of GLCM features describing relatively homogenous areas (i.e., dominant land cover or low-frequency areas) and the more heterogenous areas (i.e., edges or high-frequency areas) and non-GLCM variables.

Monitoring land use changes and its future prospects using cellular automata simulation and artificial neural network for Ahmedabad city, India

The urban sprawl is one of the potential threat for sustainable development. The present study has been carried out on Ahmedabad Municipal Corporation (AMC) area including Ahmedabad city to monitor the urban sprawl from 1976 to 2017. Normalized Difference Vegetation Index, Normalized Difference Built-Up Index, and Built-Up Index are applied to monitor the urban land use change by using multi-temporal and multi-date remote sensing satellite data from Landsat. The future scenario of urban land use prediction of AMC has been modelled by the use of cellular automata (CA) and artificial neural network (ANN) with GIS techniques. The results of land use monitoring indicate that the built-up area has increased by 156.93 km2 whereas, agricultural land, open spaces, urban vegetation, and water bodies have decreased during the last 4-decades from 1976 to 2017. Moreover, the results of CA simulation reveals that the built-up will continue to increase in the future at the cost of other classes. The decadal built-up growth was 2.58 km2 during the earlier period and it was the highest (5.98 km2) during the decade of 2007–2017. The validation of results related to CA–ANN has been measured by the Kappa coefficient in which the obtained values of Kappa local, Kappa histogram and Kappa overall were 0.92, 0.75, and 0.69 respectively and the measured percentage of correctness was 78.63%. The predicted urban land use growth reveals that the built-up would cover the maximum part of the AMC area by 2027 provided that the present land use trend, demographic growth, and commercial development does not show any major change. Therefore, the study pertaining to LULC change and its future prediction will be highly helpful for urban planners and administrators to prepare a sustainable city planning for the Ahmedabad city and its surroundings.