Normalized Difference Built-up Index Research Articles

Flood Vulnerability Assessment using Geospatial Techniques: Chennai, India

Timely and accurate damage assessment due to floods is crucial for the authorities to respond. Damage assessment and vulnerability mapping of Chennai flooding 2015 is presented. Objectives: The key objective of the paper is to quantify the extent of inundation, the damage incurred to the built environment, road and railway networks by mapping the flood vulnerable areas based on watershed analysis. Methods/Analysis: Landsat-8 OLI, Sentinel-1 and CartoDEM-3 R1 data of the study area during the flooded period were analyzed using geospatial techniques such as Normalized Difference Water Index, Normalized Difference Vegetation Index, Normalized Difference Built-up Index, Iso-Cluster Unsupervised Classification and Spatial Analyst tool. Findings: The results showed that 18% of the Chennai Metropolitan Area (CMA) (including 56 sq.km of the built fabric, 3742 km road length) was inundated directly affecting 21% of the total population. Novelty/Improvement: Based on the results, the study area is classified into low, medium and high vulnerable areas. Suitable directions for effective disaster management are recommended. Keywords: Disaster Management, Inundation, Vulnerability Mapping, Watershed Analysis

Analytical study of land surface temperature with NDVI and NDBI using Landsat 8 OLI and TIRS data in Florence and Naples city, Italy

ABSTRACTThe present study focuses on determining the relationship of estimated land surface temperature (LST) with normalized difference vegetation index (NDVI) and normalized difference built-up index (NDBI) for Florence and Naples cities in Italy using Landsat 8 data. The study also classifies different land use/land cover LU–LC) types using NDVI and NDBI threshold values, iterative self-organizing data analysis technique and maximum likelihood classifier, and analyses the relationship built by LST with the built-up area and bare land. Urban thermal field variance index was applied to determine the thermal and ecological comfort level of the city. Several urban heat islands (UHIs) were extracted as the most heated zones within the city boundaries due to increasing anthropogenic activities. The difference between the mean LST of UHI and non-UHI is 3.15°C and 3.31°C, respectively, for Florence and Naples. LST build a strong correlation with NDVI (negative) and NDBI (positive) for both the cities as a whole, especially for the non-UHIs. But, the strength of correlation becomes much weaker within the UHIs. Moreover, most of the UHIs (85.21% in Naples and 76.62% in Florence) are developed within the built-up area or bare land and are demarcated as an ecologically stressed zone.

Seasonal Comparative Study of NDVI, NDBI and NDWI of Hyderabad City (Telangana) Based on LISS-III Image Using Remote Sensing and DIP

In the present study, efforts have been made to identify and map normalised difference vegetation index (NDVI), normalised difference built-up index (NDBI)and normalised difference water index (NDWI) in Hyderabad city, Telangana, India. IRS-LISS III satellite data of four dates, namely Dec’09, Dec’11, March’12 and March’13 have been used in the study. High NDVI values were concentrated in Dec2011 of Hyderabad city. Difference between the maximum and minimum NDVI values in four dates was 1.198, 0.977, 1.984 and 1.065, respectively. High NDBI values were concentrated in Dec 2011 Hyderabad city. Difference between the maximum and minimum NDBI values in four dates was 0.997, 0.994, 1.983 and 0.722, respectively.

Landslide susceptibility assessment in the Nantian area of China: a comparison of frequency ratio model and support vector machine

It is significant to do landslide susceptibility assessment (LSA) accurately and efficiently using an appropriate model for landslide prediction and prevention. This article aims to compare the frequency ratio (FR) model with the support vector machine (SVM), for mapping the landslide susceptibility of Nantian area in southeastern hilly area, China. To begin, 70 recorded landslides are identified through field investigation and the land and recourse department, 50% of the landslide grid cells are used to train the models and the remaining 50% of the landslide grid cells are used to test the models. Ten environmental factors are used in the modeling of LSA, including the elevation, slope, aspect, plan curvature, profile curvature, relief amplitude, lithology factor, distance to river, Normalized Difference Build-up Index (NDBI) and Normalized Difference Vegetation Index (NDVI). Then the landslide susceptibility maps of Nantian area are produced by the FR and SVM models, respectively. Finally, the accuracies and efficiencies of both two models are evaluated and compared. The results show that the landslide susceptibility distribution characteristics of Nantian area are explored well by the two models, and the FR model has higher prediction rate and is considerably more efficient than SVM for LSA.

Spatio temporal analysis of urban expansion and its impact on land use land cover: A case study of Guwahati metropolitan area.

This paper presents temporal and spatial dynamics of urban expansion and its impact on the land use land cover (LULC) in Guwahati Metropolitan Area, India by using Geographical Information Systems (GIS) and Remote Sensing. The LULC changes were detected through the integration of remotely sensed Landsat imageries and GIS for the year 1991 and 2016. Normalized Difference Built-up Index (NDBI) was implemented for automated extraction of built-up areas and maximum Likelihood Supervised Classification and post-classification techniques were carried out to examine the LULC changes over a period of 25 years. The growth trend of built-up area and the LULC changes show that there are substantial exchanges of areas between vegetation and built up land along with dense forest loss occurring mainly in the hills and foothills in and around Guwahati mainly due to the need for coming up with a substantial increase in population and economic activities. The limited land availability and the confinement of forest areas to the hills have forced the utilization of higher grounds resulting in huge land use transformation from vegetated land and forests to built-up land.

Step-wise Land-class Elimination Approach for extracting mixed-type built-up areas of Kolkata megacity

The extraction of urban built-up areas is an important aspect of urban planning and understanding the complex drivers and biophysical mechanism of urban climate processes. However, built-up area extraction using Landsat data is a challenging task due to spatio-temporal dynamics and spatially intermixed nature of Land Use and Land Cover (LULC) in the cities of the developing countries, particularly in tropics. In the light of advantages and drawbacks of the Normalized Difference Built-up Index (NDBI) and Built-up Area Extraction Method (BAEM), a new and simple method i.e. Step-wise Land-class Elimination Approach (SLEA) is proposed for rapid and accurate mapping of urban built-up areas without depending exclusively on the band specific normalized indices, in order to pursue a more generalized approach. It combines the use of a single band layer, Normalized Difference Vegetation Index (NDVI) image and another binary image obtained through Logit model. Based on the spectral designation of the satellite image in use, a particular band is chosen for identification of water pixels. The Double-window Flexible Pace Search (DFPS) approach is employed for finding the optimum threshold value that segments the selected band image into water and non-water categories. The water pixels are then eliminated from the original image. The vegetation pixels are similarly identified using the NDVI image and eliminated. The residual pixels left after elimination of water and vegetation categories belong either to the built-up areas or to bare land categories. Logit model is used for separation of the built-up areas from bare lands. The effectiveness of this method was tested through the mapping of built-up areas of the Kolkata Metropolitan Area (KMA), India from Thematic Mapper (TM) images of 2000, 2005 and 2010, and Operational Land Imager (OLI) image of 2015. Results of the proposed SLEA were 95.33% accurate on the whole, while those derived by the NDBI and BAEM approaches returned an overall accuracy of 83.67% and 89.33%, respectively. Comparisons of the results obtained using this method with those obtained from NDBI and BAEM approaches demonstrate that the proposed approach is quite reliable. The SLEA generates new patterns of evidence and hypotheses for built-up areas extraction research, providing an integral link with statistical science and encouraging trans-disciplinary collaborations to build robust knowledge and problem solving capacity in urban areas. It also brings landscape architecture, urban and regional planning, landscape and ecological engineering, and other practice-oriented fields to bear together in processes for identifying problems and analyzing, synthesizng, and evaluating desirable alternatives for urban change. This method produced very accurate results in a more efficient manner compared to the earlier built-up area extraction approaches for the landscape and urban planning.

Spatiotemporal Built-up Land Density Mapping Using Various Spectral Indices in Landsat-7 ETM+ and Landsat-8 OLI/TIRS (Case Study: Surakarta City)

Spectral indices variations support for rapid and accurate extracting information such as built-up density. However, the exact determination of spectral waves for built-up density extraction is lacking. This study explains and compares the capabilities of 5 variations of spectral indices in spatiotemporal built-up density mapping using Landsat-7 ETM+ and Landsat-8 OLI/TIRS in Surakarta City on 2002 and 2015. The spectral indices variations used are 3 mid-infrared (MIR) based indices such as the Normalized Difference Built-up Index (NDBI), Urban Index (UI) and Built-up and 2 visible based indices such as VrNIR-BI (visible red) and VgNIR-BI (visible green). Linear regression statistics between ground value samples from Google Earth image in 2002 and 2015 and spectral indices for determining built-up land density. Ground value used amounted to 27 samples for model and 7 samples for accuracy test. The classification of built-up density mapping is divided into 9 classes: unclassified, 0-12.5%, 12.5-25%, 25-37.5%, 37.5-50%, 50-62.5%, 62.5-75%, 75-87.5% and 87.5-100 %. Accuracy of built-up land density mapping in 2002 and 2015 using VrNIR-BI (81,823% and 73.235%), VgNIR-BI (78.934% and 69.028%), NDBI (34.870% and 74.365%), UI (43.273% and 64.398%) and Built-up (59.755% and 72.664%). Based all spectral indices, Surakarta City on 2000-2015 has increased of built-up land density. VgNIR-BI has better capabilities for built-up land density mapping on Landsat-7 ETM + and Landsat-8 OLI/TIRS.

Online Spatial Data Analysis and Algorithm Development for Geo-scientific Applications Using Remote Sensing Data

Availability of geospatial data from online data repositories and geoportals are increasing with rapid pace. For better utilization of these heterogeneous data sets, it is required to give more attention to develop web based algorithms as well as geocomputation models that allows high-level information extraction from distributed nature of geodata. The Open Geospatial Consortium specification such as Web Processing Service (WPS) provides the solutions for integrating remote sensing data for the computation of algorithms from distributed sources in multi-user environment. The main purpose of present study is to develop and implement a WPS based geoprocesses and analysis approach using Online Image Processing System. The software framework developed during this study is a platform independent solution which is developed using free and open source technology. As a demonstrative case study, the Normalized Difference Vegetation Index and Normalized Difference Built-up Index algorithms are annotated for automatic deployment in geoscientific processing workflow environment. The Sentinel-2 data sets and polygon geometry as region of interest are stored and published as web services for input dataset in application server repository. A case study for Champawat district of Uttarakhand state of India has been demonstrated to identify vegetation greenness and built-up sites during the summer season. In order to access the functionalities of the online application, an interactive and responsive Graphical User Interface is developed in web GIS environment. The proposed approach can be very effective and useful to address the key challenges in commercial desktop based software for geoprocessing and analysis of remote sensing data.

Analyzing the role of biophysical compositions in minimizing urban land surface temperature and urban heating

The alteration of the environment at local and regional scale is often attributed to the rapid urbanization in the developing economies like India, China, etc. The land use and land cover (LULC) modification due to urbanization affect the thermal character and Surface Urban Heat Island Intensity (SUHII) of an urban area. The Greater Hyderabad Municipal Corporation (GHMC) has witnessed the drastic urbanization in the last decade and, is the sixth largest agglomeration of India. In the present study, the temporal dataset from Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM+) are used to trace the LULC, and land surface temperature (LST) changes during 2002–2015 and demarcate the fluctuation of different biophysical surfaces. Subsequently, four different indices viz. Normalized Difference Vegetation Index (NDVI), Land Surface Water Index (LSWI), Normalized Difference Built-up Index (NDBI), and Normalized Difference Bareness Index (NDBaI) are used to document the individual and collective response of the different LULC classes/biophysical surfaces. The simple linear and step-wise multiple linear regression models determined the complex and linear behaviour of the LST with the various biophysical compositions. The GHMC exhibits a rapid urban expansion resulting into increased the LST clusters that corroborate to the changing LULC pattern. The spatiotemporal changes of LULC, LST, and UHI of GHMC from 2002 to 2015 are quantified to evaluate the effects of biophysical indices on moderating or exaggerating LST. The study outlines the urbanization in the GHMC and interaction of changing LULC pattern with the local climate and urban biophysical compositions.

THE EFFECTS OF BUILT-UP AND GREEN AREAS ON THE LAND SURFACE TEMPERATURE OF THE KUALA LUMPUR CITY

Abstract. A common consequence of rapid and uncontrollable urbanization is Urban Heat Island (UHI). It occurs due to the negligence on climate behaviour which degrades the quality of urban climate condition. Recently, addressing urban climate in urban planning through mapping has received worldwide attention. Therefore, the need to identify the significant factors is a must. This study aims to analyse the relationships between Land Surface Temperature (LST) and two urban parameters namely built-up and green areas. Geographical Information System (GIS) and remote sensing techniques were used to prepare the necessary data layers required for this study. The built-up and the green areas were extracted from Landsat 8 satellite images either using the Normalized Difference Built-Up Index (NDBI), Normalized Difference Vegetation Index (NDVI) or Modified Normalize Difference Water Index (MNDWI) algorithms, while the mono-window algorithm was used to retrieve the Land Surface Temperature (LST). Correlation analysis and Multi-Linear Regression (MLR) model were applied to quantitatively analyse the effects of the urban parameters. From the study, it was found that the two urban parameters have significant effects on the LST of Kuala Lumpur City. The built-up areas have greater influence on the LST as compared to the green areas. The built-up areas tend to increase the LST while green areas especially the densely vegetated areas help to reduce the LST within an urban areas. Future studies should focus on improving existing urban climatic model by including other urban parameters.

Comparasion of NDBI and NDVI as Indicators of Surface Urban Heat Island Effect in Landsat 8 Imagery: A Case Study of Iasi

Abstract This study compares the normalized difference built-up index (NDBI) and normalized difference vegetation index (NDVI) as indicators of surface urban heat island effects in Landsat-8 OLI imagery by investigating the relationships between the land surface temperature (LST), NDBI and NDVI. The urban heat island (UHI) represents the phenomenon of higher atmospheric and surface temperatures occurring in urban area or metropolitan area than in the surrounding rural areas due to urbanization. With the development of remote sensing technology, it has become an important approach to urban heat island research. Landsat data were used to estimate the LST, NDBI and NDVI from four seasons for Iasi municipality area. This paper indicates than there is a strong linear relationship between LST and NDBI, whereas the relationship between LST and NDVI varies by season. This paper suggests, NDBI is an accurate indicator of surface UHI effects and can be used as a complementary metric to the traditionally applied NDVI.

Investigating spatial and seasonal variations of urban heat island effect over Jaipur city and its relationship with vegetation, urbanization and elevation parameters

Land Surface Temperature (LST) is one of the principal parameters for the analysis of surface urban heat island (SUHI) effect. Analysis of 8-day night-time LST data shows that significant SUHI exists over the Jaipur study area. Average maximum UHI intensity from 2003 to 2015 varies from 5.12K to 10.37K, and overall average maximum UHI intensity is 7.86K. A negative correlation exists between LST and vegetation indices. The relationship between LST and percent impervious surface area (%ISA) is almost same throughout the entire season with a consistent rising trend. A positive correlation has been found between LST and normalized difference built-up index (NDBI), especially for monsoon season. The trend of LST and NDBI relationship during the different season is different, which clearly indicates that the relationship between LST and NDBI is season dependent. NDBI cannot be used as a good indicator for SUHI studies where bare land is predominant especially for the summer season. Hence %ISA can be used as a better parameter for SUHI studies than NDBI. As the elevation increases, rising trend of LST can be observed in the study area. LST-Elevation scatterplots show a negative relationship in high altitude areas, especially during monsoon season.

Spatial and temporal distribution of urban heat islands

The formation of an urban heat island (UHI) is one of the most common impacts of the urbanization process. To mitigate the effects of UHI, the planning of urban forests (e.g., creation of parks, forests and afforestation streets) has been the major tool applied in this context. Thus, the aim of this study is to evaluate the spatial and temporal distribution of heat islands in Vila Velha, ES, Brazil using the mono-window algorithm. The study followed these methodological steps: 1) mapping of urban green areas through a photointerpretation screen; 2) application of the mono-window algorithm to obtain the spatial and temporal patterns of land surface temperature (LST); 3) correlation between LST and the normalized difference vegetation index (NDVI) and normalized difference build-up index (NDBI); 4) application of ecological evaluation index. The results showed that the mean values of LST in urban areas were at least 2.34 to 7.19°C higher than undeveloped areas. Moreover, the positive correlation between LST and NDBI showed an amplifying effect of the developed areas for UHI, while areas with a predominance of vegetation attenuated the effect of UHI. Urban centers, clustered in some parts of the city, received the worst ecological assessment index. Finally, the adoption of measures to guide the urban forest planning within urban centers is necessary to mitigate the effect of heat islands and provide thermal comfort in urban areas.

Investigating surface urban heat island characteristics over Abuja, Nigeria: Relationship between land surface temperature and multiple vegetation indices

The study is aimed at investigating urban heat island over Abuja based on the relationship between land surface temperature estimated from Landsat 8 Thermal Infrared Sensor (TIRS) band and four vegetation indices from Landsat 8 Operational Land Imager (OLI) bands. The four vegetation indices considered are the Red Edge Normalized Difference Vegetation Index 705 (NDVI705), Modified Soil Adjusted Vegetation Index 2 (MSAVI2), Ratio Vegetation Index (RVI) and Normalized Difference Built-up Index (NDBI). A linear regression is generated to evaluate the correlation of Land Surface Temperature (LST) with the four vegetation indices. LST relationships with NDVI705 and MSAVI2 showed negative correlations and low correlation coefficients (R) values while the relationships with NDBI and RVI showed positive correlations with NDBI having R value of 0.84. Negative and near-negative values NDVI705 and MSAVI2 were observed on built up areas, water bodies and bare surfaces while highest values of RVI and NDBI were recorded on built up surfaces. The trend analysis of LST against different Land use Land cover (LULC) indicated that there is a decreasing trend of LST across the sample points from bare surfaces to water bodies with R of 0.83. Significant hot spots of high LST were recorded in built up areas and bare surfaces while significant cold spots were seen on vegetated surfaces. In general, the LST, hot spots and cold spots were shown to have been greatly influenced by the LULC of the area.

An Urban Heat Island Study of the Colombo Metropolitan Area, Sri Lanka, Based on Landsat Data (1997–2017)

One of the major impacts associated with unplanned rapid urban growth is the decrease of urban vegetation, which is often replaced with impervious surfaces such as buildings, parking lots, roads, and pavements. Consequently, as the percentage of impervious surfaces continues to increase at the expense of vegetation cover, surface urban heat island (SUHI) forms and becomes more intense. The Colombo Metropolitan Area (CMA), Sri Lanka, is one of the rapidly urbanizing metropolitan regions in South Asia. In this study, we examined the spatiotemporal variations of land surface temperature (LST) in the CMA in the context of the SUHI phenomenon using Landsat data. More specifically, we examined the relationship of LST with the normalized difference vegetation index (NDVI) and the normalized difference built-up index (NDBI) at three time points (1997, 2007 and 2017). In addition, we also identified environmentally critical areas based on LST and NDVI. We found significant correlations of LST with NDVI (negative) and NDBI (positive) (p < 0.001) across all three time points. Most of the environmentally critical areas are located in the central business district (CBD), near the harbor, across the coastal belt, and along the main transportation network. We recommend that those identified environmentally critical areas be considered in the future urban planning and landscape development of the city. Green spaces can help improve the environmental sustainability of the CMA.

DETECTION OF GREEN OPEN SPACE USING COMBINATION INDEX OF LANDSAT 8 DATA (CASE STUDY: DKI JAKARTA)

Spatial information about the availability and presence of green open space in urban areas to be up to date and transparent was a necessity. This study explained the technique to get the green open spaces of spatial information quickly using an index approach of Landsat 8. The purpose of this study was to evaluate the ability of the method to detect the green open spaces, especially using Landsat 8 with a combination of several indices, namely Normalized Difference Build-up Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Build-up Index (NDBI) and Normalized Difference Bareness Index (NDBaI) with a study area of Jakarta. This study found that the detection and identification of green open space classes used a combination of index and band gave good results with an accuracy of 81%.

Modeling Urban PM2.5 Concentration by Combining Regression Models and Spectral Unmixing Analysis in a Region of East China

Understanding the spatial distribution of PM2.5 concentration and its contributing environmental variables is critical to develop strategies of addressing adverse effects of the particulate pollution. In this study, a range of meteorological and land use factors were incorporated into a linear regression (LR) model and a logistic model-based regression (LMR) model to simulate the annual and winter PM2.5 concentrations. The vegetation cover, derived from a linear spectral unmixing analysis (LSUA), and the normalized difference built-up index (NDBI), were found to improve the goodness of fit of the models. The study shows that (1) both the LR and the LMR agree on the predicted spatial patterns of PM2.5 concentration and (2) the goodness of fit is higher for the models established based on the annual PM2.5 concentration than that based on the winter PM2.5. The modeling results show that higher PM2.5 concentration coincided with the major urban area for the annual average but focused on the suburban and rural areas for the winter. The methods introduced in this study can potentially be applied to similar regions in other developing countries.

Vegetation cover change in growing urban agglomerations in Chile

Cities in Latin America expose high rates of urbanization and poorly controlled processes of creation of new urban peripheries. In this study we evaluated the changes in vegetation cover as a proxy of the success of urban planning in the creation or conservation of elements able to provide ecosystem services to citizens and therefore strengthening urban sustainability. Three urban agglomerations in Chile located in different climates were analysed. Four indicators were processed to understand the changes and correlations between vegetation and urban dynamics: normalized difference vegetation index (NDVI), vegetation cover, normalized difference built-up index (NDBI) and built-up area. The indicators were calculated for a period over 20 years covering two parts of the city as an urban development: the urban core and the new peripheries. An overall loss of vegetation was observed in all cities has a consequence of urban expansion despite their geographical location. Moreover, the greatest losses were in new peripheries. Santiago broke this pattern of change. First its urban core showed a small increase in indicators for vegetation cover despite the increase in indicators for urban dynamics. Secondly, despite their peripheries experiencing a decrease in vegetation cover, a more detailed analysis found differences on the northern and eastern peripheries where increases of vegetation cover were observed, and other new peripheries where vegetation loss was massive. Urban planning needs to play a role not only to facilitate the creation of green spaces or other public spaces able to host vegetation, but also to form an urban structure supported by regulations that facilitate the planting and maintenance of vegetation in private spaces.

THE EFFECT OF ENVIRONMENTAL CONDITION CHANGES ON DISTRIBUTION OF URBAN HEAT ISLAND IN JAKARTA BASED ON REMOTE SENSING DATA

Anthropogenic activities of urban growth and development in the area of Jakarta has caused increasingly uncomfortable climatic conditions and tended to be warmer and potentially cause the urban heat island (UHI). This phenomenon can be monitored by observing the air temperature measured by climatological station, but the scope is relatively limited. Therefore, the utilization of remote sensing data is very important in monitoring the UHI with wider coverage and effective. In addition, the remote sensing data can also be used to map the pattern of changes in environmental conditions (microclimate). This study aimed to analyze the effect of changes in environmental conditions (land use/cover, Normalized Difference Vegetation Index (NDVI) and Normalized Difference Build-up Index (NDBI)) toward the spread of the urban heat island (UHI). In this case, the UHI was identified from pattern changes of Land Surface Temperature (LST) in Jakarta based on data from remote sensing. The data used was Landsat 7 in 2007 and Landsat 8 in 2013 for parameter extraction environmental conditions, namely: land use cover, NDVI, NDBI, and LST. The analysis showed that during the period 2007 to 2013, there has been a change in the condition of the land use/cover, impairment NDVI, and expansion NDBI that trigger an increase in LST and the formation of heat islands in Jakarta, especially in the area of business centers, main street and surrounding area, as well as in residential areas.

Assessing spatiotemporal eco-environmental vulnerability by Landsat data

An indicator of quantifying eco-environmental vulnerability was established by synthesizing 12 variables, mainly retrieved from satellite data with incorporation of analytical hierarchy process (AHP). Six vulnerability levels of potential, slight, light, medium, heavy, and very heavy were graded to depict changes of vulnerability over temporal and spatial scales. The proposed approach was employed to study spatiotemporal eco-environmental vulnerability with Landsat data acquired in 1989, 2003, and 2014 for the Thua Thien – Hue Province, Vietnam. Over the time periods of 1989–2003 and 2003–2014, both heavy and very heavy vulnerability levels exhibit an increasing trend in both magnitude and spatial size: The former raised from 5.9% in 1989, to 7.9% in 2003, and 15% in 2014; and the later increased from 1.2% in 1989, to 3.2% in 2003, and 7.3% in 2014. Both levels mainly appeared on urbanized area, bare land, semi-bare land, agricultural land, and sparse forests. In contrast, there was a significant decline in potential vulnerability level with 36.4% in 1989, 30.9% in 2003, and 19.2% in 2014, while the remaining vulnerability levels slight, light, and medium fluctuated slightly, increased in 2003 and decreased in 2014. Supporting reasons for such changes include: (1) deforestation, agriculture intensification, construction of three hydro-electric projects during the period 2003–2014; and (2) significant expansion of urbanized area leading to differences in thermal signatures in urban areas as compared with rural areas. The findings demonstrate that eco-environmental vulnerability is primarily exaggerated by anthropogenic activities through land use/land cover (LULC) changes and further enhanced by natural processes including disasters in the Thua Thien – Hue Province of Vietnam. The correlation between land surface temperature (LST) and Normalized Difference Built-up Index (NDBI) is found to be positively correlated with 0.87, 0.89, and 0.84 for 1989, 2003, and 2014, respectively. In contrast, LST-Normalized Difference Vegetation Index (NDVI) is found negatively correlated with respect to the spatiotemporal trend of environmental vulnerability with −0.81, −0.81, and −0.76 in 1989, 2003, and 2014, respectively. In addition, areas having potential, slight, and medium thermal environmental levels are decreased from 1989 to 2003 to 2003–2014. At the regional scale, increased anthropogenic activities through land’s modification have intensified the eco-environmental vulnerability in the study area. The currently proposed methodology is feasible for evaluating long-term eco-environmental changes processes by using remote sensing data, and valid for the other regions.