Analyzing Ecological Environmental Quality Trends in Dhaka Through Remote Sensing Based Ecological Index (RSEI)

Abstract

Assessing the ecological environmental quality (EEQ) is crucial for protecting the environment. Dhaka’s rapid, unplanned urbanization, driven by economic and social growth, poses significant eco-environmental challenges. Spatiotemporal ecological and environmental quality changes were assessed using remote sensing based ecological index (RSEI) maps derived from Landsat images (1993, 2003, 2013, and 2023). RSEI was based on four indicators—greenness (NDVI), heat index (LST), dryness (NDBSI), and wetness (LSM). Landsat 5 TM and 8 OLI/TIRS images were processed on Google Earth Engine (GEE), with principal component analysis (PCA) applied to determine RSEI. The findings showed a decline in the overall RSEI (1993–2023), with low- and very low-quality areas increasing by about 39% and high- and very high-quality areas decreasing by 24% of the total area. NDBSI and LST were negatively correlated with RSEI, except in 1993, while NDVI and LSM were generally positive but negative in 1993. The global Moran’s I (0.88–0.93) indicated strong spatial correlation in the distribution of EEQ across Dhaka. LISA cluster maps showed high-high clusters in the northeast and east, while low-low clusters were concentrated in the northwest. This research examines the degradation of ecological conditions over time in Dhaka and provides valuable insights for policymakers to address environmental issues and improve future ecological management.

1. Introduction

The ecological environment refers to all the elements influencing the preservation and evolution of ecosystems [1]. It is closely related to the worth of the surroundings for human welfare and socio-economic viability [2]. Human activities, including civilization, industrialization, and environmental policies, directly impact ecosystems [3,4]. Changes in land cover due to human activities influence habitat distribution as well as biodiversity, ecological processes, and surface temperature, altering ecological settings and functions [5,6]. As a result, many environmental challenges, for example, the loss of ecological functions, land desertification, soil erosion, environmental contamination, and the degradation of biodiversity, have surfaced. These issues have increased the fragility of the environment, which is vital to human life [7].

Urbanization alters environments, intensifying heat island effects and pollution, as well as harming ecosystems and human health. Rapid urbanization promotes economic and social development; however, this growth also leads to numerous environmental issues on both local and global levels [8,9]. Uncontrolled urbanization results in serious problems, including social and ecological degradation, destruction of biodiversity and ecosystems, and health crises [8,10].

Bangladesh’s rapid urbanization, faster than most South Asian countries, has triggered severe ecological impacts in major cities [11,12,13,14]. Based on the night light intensity, Rahman et al. [11] grouped 331 Bangladeshi cities; Dhaka fell into the same category as seven other big cities. The growth of urban areas in Dhaka City has led to habitat destruction and fragmentation, adversely affecting wildlife diversity. The aquatic ecosystem, a crucial element of overall livelihood, is gradually being destroyed due to overexploitation, pollution, and climate change, posing significant threats to human well-being and environmental sustainability [15]. Moreover, pollution in this city severely impacts its fragile ecosystem, leading to extensive ecological degradation [16]. Consequently, analyzing how urban development affects a city’s environmental and thermal environment helps authorities design a more ecologically friendly urban development. Changes in the city’s land use have deteriorated the ecosystem service value and reduced the overall ecosystem service value over the past thirty years [17]. Furthermore, preserving urban ecosystems is vital for maintaining the health of both humans and the natural environment [18]. As a result, studying urban ecology has become a critical necessity in the current context.

Therefore, there is a rising demand for models that can detect spatial and temporal variations in ecological status [19]. Recent developments in satellite-based Earth observation systems can serve as powerful tools for ecosystem management, offering robust indicators of ecosystem conditions from local to global scales [20]. Measuring the reflected radiation from the Earth’s surface allows one to assess ecological conditions at several levels using remote sensing images. This capability enables the detection of various ecosystem components, including soil, vegetation, and open water [21,22,23]. As a result, remote sensing technology is extensively applied in ecosystem research and governance [24,25].

Numerous research articles focus on urban ecology assessment, including the monitoring of built-up and vegetation [26,27], urban heat islands (UHIs) [28,29], water resource management [30], air quality regulation [31], and biodiversity monitoring [32]. These studies underscore the effectiveness of remote sensing methods in urban ecology monitoring [33,34], employing various indices such as NDVI [35], LST [36], BI [37], EVI [38], NDBI [39], SAVI [40], and MNDWI [41]. However, Xu [42] introduced the remote sensing based ecological index (RSEI) to observe and evaluate regional ecological conditions. This index can enable quick identification and evaluation of regional environmental conditions over time [43]. These studies have found four main factors of the ecological environment: greenness, heat index, dryness, and wetness. Studies have given critical new perspectives on environmental changes and weaknesses in many areas, including Tianjin City [44], Samara [45], Xiong’an New Area [25], Fuzhou City [46], Manas Lake wetland area [47], and the coastal regions of southeastern China [19], as well as other cities in China [48]. These studies have also emphasized the impact of socioeconomic and human activities on the ecological environment and the potential of plant restoration to improve ecological conditions [48].

However, according to the authors’ literature review, there are no significant studies utilizing the RSEI approach to evaluate the ecological conditions in Dhaka, Bangladesh, though there is a substantial threat to the ecology in this area. This research is unique because it focuses on filling a specific gap in the existing studies. The main objective of this study is to utilize RSEI methodologies to assess the ecological environment in Dhaka city. Four parameters, namely greenness (NDVI), heat index (LST), wetness (LSM), and dryness (NDBSI), were utilized to calculate the RSEI for the Dhaka metropolitan area. This research uses satellite imagery to analyze alterations in the ecological environment over the past three decades. It aims to evaluate the impact of rapid urbanization on urban ecology and propose solutions to address environmental vulnerability in the capital city of Bangladesh.

Study Area

Dhaka, the capital of Bangladesh, functions as the center for the nation’s political, economic, and cultural activities. It is the largest city in the country and ranks as the 11th largest megacity globally. It is located on the northern bank of the Buriganga River, inside the Ganges delta, and is bordered by other rivers [49]. The Buriganga River borders it to the south, and the Turag and Balu Rivers border it to the north and northeast. Tongi and Baru mark the northeastern boundary. Dhaka’s elevation varies from approximately 0 to 23 m above sea level, with an average altitude of 7 m. The city receives approximately 1854 mm of average annual rainfall, with 80% of this precipitation occurring between May and September [50]. The Dhaka metropolitan area has a population of approximately 22.4 million and a population density exceeding 23,000 people per square kilometer, making it one of the most densely populated regions globally [51]. Dhaka has developed substantially in recent years, establishing itself as the fastest-growing major city [52]. Recent urbanization and development have made Dhaka City’s ecology vulnerable. Infrastructure growth, wetland infill, deforestation, and pollution from poor waste management have significantly threatened its ecological balance (Figure 1).

2. Materials and Methods

Initially, the image datasets were cleaned up for clouds (less than 1%), cloud shadows, and snow/ice pixels to ensure completeness and reliability for subsequent analysis. Data have been produced from the Landsat 5 TM. Landsat 8 OLI top-of-atmosphere sensors (LANDSAT/LT05/C02/T1 and LANDSAT/LC08/C02/T1) were used to compute various indices, including greenness (NDVI), wetness (LSM), dryness (NDBSI), and heat index (LST) (Figure 2). Subsequently, these data were used to create four 30 m resolution RSEI maps for the years 1993, 2003, 2013, and 2023 using Python 3 in Google Colaboratory. The subsequent phase entailed examining the temporal and geographical variations in the ecological environment quality (EEQ) in Dhaka city by utilizing the four RSEI maps from 1993 to 2023. Afterwards, the spatial correlation of EEQ+ was assessed using the global spatial autocorrelation index, such as Moran’s I, and the Local Indicators of Spatial Association (LISA). The specific information on the datasets can be found in

Table 1. The data sources for satellite images used in the current research.

No.	Acquisition Date	Satellite	GEE Product Identifier	Cloud Cover
1	January, 1993	Landsat 5 TM	LANDSAT/LT05/C02/T1_TOA	<1%
2	January, 2003	Landsat 5 TM	LANDSAT/LT05/C02/T1_TOA	<1%
3	January, 2013	Landsat 8 OLI/TIRS	LANDSAT/LC08/C02/T1_TOA	<1%
4	January, 2023	Landsat 8 OLI/TIRS	LANDSAT/LC08/C02/T1_TOA	<1%

. The detailed workflow of this research is shown in Figure 3.

2.1. Greenness Index

The normalized difference vegetation index (NDVI) is a crucial metric for assessing vegetation cover, renowned for its effectiveness among various forest indices. NDVI values have been employed to forecast bird diversity, urban tolerance, and specialization in urban environments, hence highlighting its significance in efficiently monitoring biodiversity trends [53]. The calculation of NDVI has been illustrated in Equation (1), highlighting its wide application in measuring forest density and its usefulness in many ecological studies [54,55].

$$NDVI={\displaystyle \frac{NIR-RED}{NIR+RED}}$$

(1)

In the $NDVI$ formula, “NIR” indicates the near-infrared red band, and “RED” refers to the red band of light. The spatiotemporal NDVI images are given in Figure 2.

2.2. Wetness Index

Land surface moisture (LSM) has a substantial impact on ecology since it plays a pivotal role in a range of ecological processes and the evolution of ecosystems [56,57]. Specifically, for Landsat Operational Land Imager (OLI) and Landsat Thematic Mapper (TM) data, the wetness index can be accurately estimated using [58] Equations (2) and (3) [59], respectively. The wetness images are given in Figure 2.

$$\begin{gathered} LSM-TM=0.3102\times Red+0.2021\times Green+0.0315\times Blue+0.1594\times NIR- \\ 0.6806\times SWIR1-0.6109\times SWIR2\dots \end{gathered}$$

(2)

$$\begin{gathered} LSM-OLI=0.3283\times Red+0.1972\times Green+0.1511\times Blue+0.3407\times NIR \\ -0.7117\times SWIR1-0.4559\times SWIR2 \end{gathered}$$

(3)

2.3. Dryness

Dryness characterizes regions with minimal vegetation or low soil moisture. Human activities and uncontrolled urbanization have resulted in the substitution of natural land surfaces and forest cover with urban areas and exposed soil, hence exacerbating environmental deterioration [60]. Therefore, this study selected the normalized difference build-up and soil index (NDBSI) in Figure 2, associated with the Index-based Built-up Index (IBI) and Soil Index (SI). The following Equations (4)–(6) measure these indicators [61,62,63]:

$$NDBSI=(IBI+SI)/2$$

(4)

$$IBI={\displaystyle \frac{\frac{2SWIR1}{(SWIR1+NIR)}-\frac{NIR}{\left(NIR+Red\right)}-\frac{Green}{(Green+SWIR1)}}{\frac{2SWIR1}{(SWIR1+NIR)}+\frac{ NIR}{(NIR+Red)}+\frac{Green}{(Green+SWIR1)}}} \dots$$

(5)

$$SI=\left[\right(SWIR1+Red)-(NIR+Blue\left)\right]/\left[\right(SWIR1+Red)+(NIR+Blue\left)\right]$$

(6)

where $SWIR1, NIR, Red, Green, and Blue$ represent the reflectance of each band in the TM and OLI sensors, respectively.

2.4. Heat Index

The land surface temperature (LST) is a key component of the heat index and plays a critical role in the RSEI (Figure 2), acting as a determinant of the temperature of the Earth’s surface [64,65]. It spans numerous fields, involving climate research, agriculture, UHI investigations, water resource management, and eco-environmental studies. Thermal infrared bands from satellite sensors are essential for LST computation [66,67,68,69]. In this study, LST was derived using standard procedures for Landsat 5 TM and Landsat 8 OLI/TIRS, including radiometric calibration, brightness temperature conversion, and emissivity correction. The detailed calculation formulas and processing steps are provided in Supplementary Materials (Sections S1–S7).

2.5. Calculation of RSEI

The RSEI, introduced by Xu [42], serves as an efficient model for monitoring and evaluating ecological conditions, utilizing solely remotely sensed data [19]. It is widely acknowledged for assessing environmental quality and incorporates four critical components: greenness (NDVI), heat index (LST), dryness (NDBSI), and wetness (LSM). These components are extracted from satellite imagery through inversion techniques [70].

Principal component analysis (PCA) is used to combine these four indicators. PCA is used because it can compress multi-dimensional data and identify significant factors by transforming numerous variables in an orthogonal linear manner [71]. The benefit of PCA is its capacity to automatically and empirically decide the weight of each indicator based on the data features and the contribution rate of each index to each principal component [72]. The impact of each indication on the RSEI is determined by its weighting on PC1 [25]. The RSEI is formulated as follows:

$$RSEI=PC1\left[f\right(\mathrm{L}\mathrm{S}\mathrm{T},\mathrm{N}\mathrm{D}\mathrm{B}\mathrm{S}\mathrm{I},\mathrm{N}\mathrm{D}\mathrm{V}\mathrm{I},\mathrm{L}\mathrm{S}\mathrm{M}\left)\right]$$

(7)

Before the PCA calculation, all index values were normalized to a 0 to 1 scale to eliminate negative values and ensure comparability across factors. Equation (8) is used for the normalization process, which is given below:

$$NIi=(Ii-Imin)/(Imax-Imin)$$

(8)

where NI_i represents the normalized index value; I_i is the index value for pixel I; I_min is the index’s minimum value; and Imax is this index’s maximum value.

The RSEI values derived from this equation are such that lower values indicate better ecological conditions, while higher values signify poorer conditions [19]. To align with the standard expectation that higher RSEI values should denote better ecological status, the RSEI is adjusted as follows:

$$\mathrm{R}\mathrm{S}\mathrm{E}\mathrm{I}0=1-\left[PC1\right[f\left(\mathrm{L}\mathrm{S}\mathrm{T},\mathrm{N}\mathrm{D}\mathrm{B}\mathrm{S}\mathrm{I},\mathrm{N}\mathrm{D}\mathrm{V}\mathrm{I},\mathrm{L}\mathrm{S}\mathrm{M}\right)\left]\right]$$

(9)

Moreover, to ensure the comparability of RSEI values across different temporal and spatial contexts, normalization of the RSEI values is required:

$$\mathrm{R}\mathrm{S}\mathrm{E}\mathrm{I}=(\mathrm{R}\mathrm{S}\mathrm{E}\mathrm{I}0-\mathrm{R}\mathrm{S}\mathrm{E}\mathrm{I}0\_\mathrm{m}\mathrm{i}\mathrm{n})/(\mathrm{R}\mathrm{S}\mathrm{E}\mathrm{I}{0}_{\mathrm{m}\mathrm{a}\mathrm{x}}-\mathrm{R}\mathrm{S}\mathrm{E}\mathrm{I}0\_\mathrm{m}\mathrm{i}\mathrm{n})\dots$$

(10)

The RSEI determined using Equation (10) represents the final results of RSEI in this study. The RSEI is categorized into five classes at equal intervals (

Table 2. RSEI value classification for ecological quality.

Data Range	Class Name
0–0.20	Very Low
0.20–0.40	Low
0.40–0.60	Moderate
0.60–0.80	High
0.80–1.0	Very High

), corresponding to very low, low, moderate, high, and very high ecological environments [19,72].

2.6. Spatial Autocorrelation Analysis

Spatial autocorrelation is an essential measure for assessing the association between the environmental quality of one region and the ecological quality of its neighboring areas [73,74]. Analyzing the spatial correlation of EEQ helps describe its spatial distribution homogeneity within the study region. This research devoted both global spatial autocorrelation (Global Moran’s I) and local spatial association indicators (Local Moran’s I) to evaluate the geographical correlation of RSEI [75]. Moran’s I measures the correlation between adjoining units in geospatial space (pixels of 500 m × 500 m). A value closer to 1 indicates a stronger correlation between units. Consequently, the researchers used Moran’s I to assess the connection between the RSEI units in this study, as represented by Equation (11).

$$Globalmoran’s I={\displaystyle \frac{m\times {\sum }_{i=1}^m{\sum }_{j=1}^{m}Wij\left(Di-\overline{D}\right)\left(Dj-\overline{D}\right)}{{{\sum }_{i=1}^m{\sum }_{j=1}^{m}Wij(Di-\overline{D})}^2}}\dots$$

(11)

Here m expresses the total elements, D_i illustrates the EEQ value at position i, and $\overline{D}$ is the average EEQ value across all elements in the study region. W_ij is the spatial weight. The range of Moran’s I extends from −1 to 1. A Moran’s I value around +1 implies a high level of positive spatial autocorrelation for EEQ. Conversely, a value near −1 suggests a significant negative geographical correlation. A value of 0 signifies no spatial autocorrelation [71].

The LISA index is a crucial metric for analyzing local spatial autocorrelation, as it allows for calculating Moran’s I value at each spatial unit. This makes the analysis of local spatial autocorrelation particularly important. If there is no global spatial autocorrelation, LISA can be used to detect local spatial autocorrelation that may be concealed. LISA may be used to identify spatial heterogeneity when global spatial autocorrelation is present [76]. The calculation formula for Local Moran’s I uses the same parameters as the global Moran’s I index.

$$Local moran’s I={\displaystyle \frac{(Di-\overline{D})\times {\sum }_{j=1}^{m}Wij(Dj-\overline{D})}{{{\sum }_{i-1}^m(Di-D)}^2}}\dots$$

(12)

The LISA cluster map categorizes local spatial classes into five types: High-High (H-H), Low-Low (L-L), Low-High (L-H), High-Low (H-L), and No Significant. The H-H classification denotes that the focal and surrounding areas exhibit high environmental ecological quality (EEQ). Conversely, the L-L classification indicates that the chosen area and its neighboring areas have low EEQ. The L-H categorization refers to a situation in which the selected location has a low Environmental Equity Quotient (EEQ), while the surrounding areas have a high EEQ. In contrast, the H-L classification signifies that the chosen area has high EEQ, while the surrounding areas have low EEQ [75].

2.7. Pearson’s Correlation Analysis

It is crucial to understand the relationship between the RSEI and its constituent factors (NDVI, LST, NDBSI, and LSM) across different time frames. Pearson correlation offers a quantitative measure of the linear relationship among these variables, aiding in comprehending their interrelations and ecological impacts. The correlation coefficient spans between −1 and 1. Values near 1 suggest an intense positive linear association, values closer to −1 show a severe negative linear relationship, and values near zero show no linear link. This study aims to identify the indicators that have the most significant impact on the ecological equation in Dhaka, as defined by [77]. These indicators were then used to calculate the correlation coefficients.

$$\rho ={\sum }_{i=1}^N\left(x_i-\overline{x}\right)\left(y_i-\overline{y}\right)/\sqrt{{\sum }_{i=1}^N{(x_i-\overline{x})}^2 {\sum }_{i=1}^N{(y_i-\overline{y})}^2}$$

(13)

The Pearson correlation coefficient is denoted as $\rho$. It calculates the linear relationship between two variables. $N$ represents the spatial units; $x_i$ and $y_i$ correspond to the values of the variables and the RSEI for each unit, respectively; $\overline{x}$ represents the variable’s mean; and $\overline{y}$ signifies the RSEI mean value.

3. Results

3.1. Ecological Indicators and PCA Analysis

Four ecological factors—greenness (NDVI), heat index (LST), wetness (LSM), and dryness (NDBSI)—were used to evaluate Dhaka’s EEQ from 1993 to 2023. Principal component analysis (PCA) was applied to integrate these indicators and calculate the RSEI. The first principal component (PC1) captured most of the variance and effectively summarized the combined influence of the four ecological factors.

The results of the PCA are presented in Table 2. Based on the data, it can be shown that PC1 consistently exhibits the greatest eigenvalue among the four principal components during the whole research period, ranging from 58% to 66%. This illustrates that PC1 encompasses most of the variance in information derived from the four measures. Moreover, this study demonstrates that the four measures inside PC1 are categorized into two groups depending on their indications. The coexistence of opposing indicators in these categories indicates an inverse relationship between their importance and ecological state. The contributions of the four ecological components to PC1 remained fairly consistent across the years 1993, 2003, 2013, and 2023, as shown by the data for these years (

Table 3. PCA results of RSEI in 1993, 2003, 2013, and 2023, respectively.

Year	Indicators	PC1	PC2	PC3	PC4
1993	NDVI	0.286	−0.655	0.680	−0.163
	LSM	0.562	−0.458	−0.687	−0.038
	NDBSI	−0.567	−0.298	−0.224	−0.735
	LST	0.530	0.521	0.122	−0.658
	Eigenvalue	0.026	0.012	0.003	0.000
	Percent eigenvalue	62.00%	29.95%	7.20%	0.85%
2003	NDVI	−0.212	−0.957	−0.137	−0.143
	LSM	0.341	0.052	−0.938	0.046
	NDBSI	0.809	−0.141	0.262	−0.507
	LST	−0.429	0.248	−0.184	−0.849
	Eigenvalue	0.018	0.008	0.002	0.000
	Percent eigenvalue	62.69%	29.76%	6.21%	1.35%
2013	NDVI	−0.456	−0.853	−0.097	−0.237
	LSM	0.601	−0.241	−0.761	0.021
	NDBSI	0.646	−0.316	0.601	−0.348
	LST	−0.115	0.339	−0.223	−0.907
	Eigenvalue	0.020	0.010	0.004	0.000
	Percent eigenvalue	58.53	30.46	10.40	0.61
2023	NDVI	0.488	0.756	0.266	−0.344
	LSM	−0.584	0.091	0.806	−0.006
	NDBSI	−0.640	0.415	−0.513	−0.393
	LST	0.102	−0.498	0.124	−0.852
	Eigenvalue	0.025	0.009	0.004	0.000
	Percent eigenvalue	66.06%	23.13%	9.82%	0.99%

). The positive loadings of LST and NDVI constantly indicate the advantageous impacts of vegetation abundance and moisture content on the natural ecosystem. In 2023, the loadings for NDVI and wetness were 0.488 and −0.584, respectively. These loadings accounted for 66.06% of the total variation. In contrast, NDBSI and LST show a steady load decrease due to the detrimental impacts of exposed soil, building activities, and elevated surface temperatures. The loadings for LST and NDBSI in 2023 were 0.102 and −0.640, respectively.

These findings corroborate the widely acknowledged concept of a reciprocal link between these factors and EEQ. The selection of PC1 as the basis for creating the RSEI was based on its substantial explanatory capability. This study effectively detected dynamic changes in the four ecological components by monitoring the fluctuations in PC1 across time. The research region was characterized mainly by vegetation and exposed soil, with the dryness index (NDBSI) making the most significant overall contribution, followed by the greenness index (NDVI). In 2003, the loadings for NDBSI and NDVI were 0.809 and −0.212, respectively. These values accounted for 62.69% of the overall variation. The results highlight the practical usefulness of the RSEI in evaluating the efficiency of widespread ecological management techniques in urban settings.

3.2. Dynamic Changes in the EEQ

The spatiotemporal evolution of the ecological quality over 30 years is depicted in Figure 4. Based on the findings, ecological quality was categorized into five equal-interval classes: very poor (0–0.2), poor (0.2–0.4), moderate (0.4–0.6), good (0.6–0.8), and very good (0.8–1) [78,79]. The proportions of each ecological quality level (very low, low, moderate, high, and very high) and the area were calculated for 1993, 2003, 2013, and 2023 (Figure 5). The results indicated that from 1993 to 2023, the total proportions of areas are moving in an increasing trend toward very low and low categories, while a decreasing trend was noted for moderate, high, and very high categories; such degradation sustained over time denotes significant ecological deterioration at the general level.

Table 4. Area percentages of RSEI Classes.

Class	1993		2003		2013		2023
	Area (km2)	Percentage	Area (km2)	Percentage	Area (km2)	Percentage	Area (km2)	Percentage
Very low	0.26	0.08	0.66	0.20	1.92	0.58	5.56	1.70
Low	37.85	11.53	58.64	17.87	85.63	26.09	161.63	49.25
Moderate	162.15	49.41	158.42	48.28	150.51	45.87	111.64	34.02
High	113.17	34.49	102.04	31.10	86.17	26.26	46.77	14.25
Very high	14.72	4.49	8.40	2.56	3.93	1.20	2.55	0.78
Total	328.15	100	328.15	100	328.15	100	328.15	100

presents the changes in both area (km²) and percentage share for each ecological quality class over time.

Analysis of the dynamic variations in the ecological quality of the environment at ten-year intervals shows significant changes taking place in the area under study. The area marked as very low quality, which amounted to 0.26 km² (0.08%), reached 0.66 km² (0.20%), while the low-quality area expanded from 37.85 km² (11.53%) to 58.64 km² (17.87%). At the same time, areas of moderate quality decreased slightly from 162.15 km² (49.41%) to 158.42 km² (48.28%), high-quality areas reduced from 113.17 km² (34.49%) to 102.04 km² (31.10%), and very high-quality regions reduced from 14.72 km² (4.49%) to 8.40 km² (2.56%). Between 2003 and 2013, the very low-quality area increased further to 1.92 km² (0.58%), and the low-quality areas increased. On the other hand, moderate-quality areas declined to 150.51 km² (45.87%), high-quality areas to 86.17 km² (26.26%), and very high-quality areas to 3.93 km² (1.20%). From 2013 to 2023, the low-quality area increased significantly to 5.56 km² (1.70%), whereas low-quality areas nearly doubled to 161.63 km² (49.25%). Meanwhile, moderate-quality areas decreased to 111.64 km² (34.02%), high-quality areas declined to 46.77 km² (14.25%), and very high-quality areas further reduced to 2.55 km² (0.78%).

From 1993 to 2023, very low- and low-quality regions experienced an intense increase in the study area. This specifies an increased region of bad ecological quality, probably due to intensified urbanization and industrial activities. Concretely, from 1993 to 2023, low and very low quality regions increased by 5.30 km², while low quality areas expanded by 123.78 km². This trend significantly reduced moderate-, high-, and very high-quality areas by 50.51 km², 66.40 km², and 12.17 km², respectively. These changes indicate severe losses in regions with relatively good ecological conditions, likely due to urban expansion and environmental degradation, which cut them to a fraction of their initial extent. These results highlight the compelling need for proper implementation of environmental management and conservation to reduce further ecological degradation and conserve environmental health.

3.3. Correlation Analysis Between RSEI Variables

Table 5. The Pearson correlation between RSEI and four indices.

Year	LSM	LST	NDBSI	NDVI
1993	−0.82	−0.82	0.92	−0.49
2003	0.89	−0.75	−0.98	0.31
2013	0.38	−0.85	−0.88	0.59
2023	0.31	−0.88	−0.90	0.73

shows the Pearson correlation coefficients between the RSEI and four key environmental indicators—NDVI, NDBSI, LSM, and LST—over a 30-year period: 1993, 2003, 2013, and 2023 in Dhaka, Bangladesh.

The data showed a modest negative correlation value of −0.49 between RSEI and NDVI for 1993; hence, the value denotes a modest inverse relationship. The correlation coefficient was strongly negative for RSEI against LSM (wetness) at −0.82, whereas it denoted a strong inverse relationship with considerable values. The association with NDBSI was strongly positive, 0.92, and robustly direct, while the correlation with LST was also strongly negative, −0.82. By 2003, the RSEI correlations with NDVI had changed to a positive 0.31, reflecting a mild direct relationship. The LSM correlation showed a strong positive relationship at 0.89, suggesting a substantial direct relationship with RSEI. The relation with NDBSI had become strongly negative at −0.98, meaning it had become almost perfectly inverse. The correlation with LST remained strongly negative at −0.75. In 2013, the positive relationship between RSEI and NDVI grew to 0.59; hence, it became more direct. The relation with LSM was moderate at 0.38. Those with NDBSI and LST remained strongly negative, at −0.88 and −0.85, respectively. By 2023, the correlation of RSEI with NDVI was significantly high to the extent of 0.73, portending a straightforward relationship, while that with LSM is relatively low but positive at 0.31. The correlations with NDBSI and LST remained strongly negative at −0.90 and −0.88, respectively.

These results indicate dynamic changes over the years in the relationships between RSEI and the environmental indicators that reflect changes in vegetation, moisture levels, urban development, and temperature. These correlations’ varying strengths and directions indicate a complicated interaction between these factors in different temporal contexts.

3.4. Spatial Autocorrelation Analysis of RSEI

A spatial autocorrelation analysis was performed for Dhaka to examine the possible spatial relationship of the RSEI over various periods. This analysis utilized Moran’s I index and LISA for 1993, 2003, 2013, and 2023. The scatter diagrams of Moran’s I, shown in Figure 6, indicate that most scatter points are concentrated in each year’s first and third quadrants. This suggests a strong positive spatial correlation of EEQ within the study area. Specifically, Moran’s I indices were recorded at 0.907 in 1993, 0.883 in 2003, 0.993 in 2013, and 0.939 in 2023. This pattern indicates that the spatial distribution of EEQ is characterized by significant clustering, with the most pronounced positive spatial correlation occurring in 2023. Notably, the temporal trend of Moran’s I index reflects a preliminary decline from 1993 to 2003, followed by a subsequent increase from 2003 to 2023, mirroring the fluctuations in the levels of ecological environmental quality over these years.

To comprehend the spatiotemporal patterns of environmental equity, an analysis was conducted on the local spatial correlation of the RSEI indicators using LISA clustering maps. The findings in Figure 7 show that the H-H clustering was predominant in Dhaka’s northeastern and eastern areas. In contrast, the L-L clustering dominated the northwestern regions from 1993 to 2023. Remarkably, it was also evident that, over those years, the L-L clustering further extended towards the parts of the study region that were western in orientation, suggesting the ecological deterioration associated with urbanization. Besides this, statistically insignificant regions were randomly distributed in nature, with low-high and high-low regions nearly nonexistent.

4. Discussion

The aim of this study was to evaluate the ecological conditions in Dhaka, Bangladesh, for a period of 30 years (1993–2023) using the RSEI. PCA was used to combine the four main indicators—heat index (LST), wetness (LSM), greenness (NDVI), and dryness (NDBSI)—into a single measure of EEQ. Investigating the key findings shows that the first principal component (PC1) dominates and captures 58% to 66% of the total variance across the study years, indicating that PC1 successfully assembled information from the four sub-indicators. On the other hand, the adverse effects of bare soil, construction activities, and higher surface temperatures are reflected in the negative loadings of NDBSI across study years, as shown in Table 3. This finding is consistent with previous studies indicating that increased bare soil and impervious surfaces are associated with ecological degradation and urban heat effects [80].

Dhaka has seen a fast population increase, wetlands and agricultural land conversion into built-up areas, and industrial sprawl over the past three decades, all of which define a highly intense and often uncontrolled urban expansion. Regardless of several government projects, including the Detailed Area Plan (DAP, completed in 2010 and revised later) and the Dhaka Metropolitan Development Plan (1995–2015), enforcement has been weak, resulting in significant environmental damage. Low-quality (L-L) clusters’ westward spread matches the rising informal settlements and industrial activity along Dhaka’s western periphery.

Explaining 58–66% of the ecological variability, PC1’s consistent dominance emphasizes the significant combined impact of surface moisture, urban dryness, and vegetation cover on Dhaka’s environment. The spatial autocorrelation results, particularly the westward expansion of Low–Low (L-L) clusters, reflect a pattern of increasing population density and declining green space, largely driven by poorly managed urbanization. The almost-complete absence of Low-High (L-H) and High-Low (H-L) clusters suggests that, driven by contiguous zones of urban development rather than scattered ecological transitions, Dhaka’s environmental conditions are generally regionally homogeneous.

Our findings align with previous RSEI-based studies in China and India, confirming that urbanization degrades the ecological quality [81]. In other studies, Wang et al. [82] also assessed changes in the ecosystem service values for Zhoushan Archipelago, China, and the results were somewhat similar; a positive relation of ecosystem services with vegetation and water areas was observed, as in the case of Dhaka, where NDVI and wetness had positive effects. This further confirms the essential role of vegetation and water bodies in sustaining ecological health. Chen et al. [83] revealed that the areas of good and excellent ecological conditions in most places across the Greater Khingan Range, China, were surrounded by vegetation; poor conditions mainly fell in the city areas. This is quite similar to the pattern seen in Dhaka; the urbanization scenario seems to affect the ecological quality. The influence of eco-parameters on LST in Xi’an, China—analyzed by [84,85]—showed that greenness and soil moisture were negatively correlated with LST, while dryness and built-up features had a positive correlation with LST. These results support those found in Dhaka, emphasizing the role of green spaces in mitigating urban heat effects. Yue et al. [48] indicated that substantial changes in ecological quality were measured among most of China’s major cities due to industrialization and urbanization, thereby establishing support for the observed downfall in high- and very high-quality areas in Dhaka. This underscores the critical importance of sustainable urban development practices in preserving ecological quality. Temporal consistency was also discovered under a time-series RSEI approach in assessing ecological quality by Sun et al. [86] from Hangzhou; moreover, they also observed spatial polarization, similar to the spatial autocorrelation patterns observed within Dhaka. Ecological trends, both positive and negative, have been identified using the RSEI with Change Vector Analysis (CVA) method in Fujian Province, China, by Xu et al. [19], again proving that there is a dual effect due to human activities. It goes without saying that both urbanization and industrialization in Dhaka contributed much to ecological worsening. Regional differences in China’s environmental quality and the colossal roles of human activities were highlighted by Li and Jiang [87]. In another comparative study [88] conducted by Halder and Bose in Indian urban centers, the use of RSEI and Comprehensive Ecological Evaluation Index (CEEI) indicates that multiple ecological indicators need to be included for a comprehensive evaluation. These findings are consistent with the previously mentioned results from Dhaka, where many variables were combined to deliver a more comprehensive assessment of the ecological quality. Zhang et al. [44] quantified the ecological environment degradation in the Yangtze River basin between 2008 and 2019, concluding that it has experienced a continuous and highly significant deterioration. A similar pattern of sustained ecological degradation was also observed in Dhaka during the study period, highlighting the urgent need to develop effective conservation strategies. Jia et al. [89] used the MRSEI to assess eco-environmental quality in the Qaidam Basin, China, revealing regional variations. They also highlighted the importance of remote sensing technologies in identifying these regional characteristics. Chen et al. [90] measured the alterations taking place in the Zhoushan Archipelago’s ecological surroundings. Their findings showed a general decline in ecological quality over the course of 35 years. These findings of substantial ecological destruction due to urbanization and industrial operations align with the trends mentioned above that are visible in Dhaka. This further underscores the need to implement effective development measures. Ding et al. [91] developed a methodology for evaluating the quality of ecosystems in Guangdong province using remote sensing data and spatial autocorrelation measures. Dynamic spatial organization patterns were determined to capture hotspots and cold spots of an ecological nature, following the footsteps of spatial autocorrelation analysis performed in Dhaka, or it could also mean that the results should not be interpreted without reference to a specific context. Halder and Bose [60], for instance, studied the ecological quality across five smart cities of India and stated that the ecological environment in Kochi and New Delhi will be superior compared to the others since they have a higher percentage share of greenness and wetness areas. This finding aligns with the observations in Dhaka, emphasizing the crucial role of green spaces and water bodies in maintaining urban ecological health. Sekertekin [64] evaluated the ecological quality of the environment in the Asansol Municipal Corporation region of India, an area severely impacted by urban expansion and industrialization. The results, therefore, confirm observations made in Dhaka and the necessity for targeted ecological management strategies there. As determined by the study conducted on seasonal optimum selection for RSEI construction conducted by Huang et al. [71] in the Beijing–Tianjin–Hebei region, the images should be chosen during times of active growth of the vegetative components. This favors what is happening in Dhaka, where the strength of vegetation contributes to the ecological quality studies. An assessment of ecological environment vulnerability in Pingtan Island by Huang et al. [71] revealed the significant impact of human development activities on the region’s environment. The results show that the initial stages of construction were associated with ecological decline, followed by recovery due to ecological measures. This pattern, which is also observed in Dhaka, reinforces the argument for balanced development and environmental conservation. The authors of [92] conducted similar research in China, focusing on temporal variations in NDVI-based time series data. Their findings showed notable ecological improvements in some areas, while signs of urban degradation were also evident in others. Their emphasis on the impact of urbanization, therefore, brings the idea of balanced development, which only affirms what is realized in Dhaka, where a critical need is exposed in sustainable urban planning. Studies in the Golden Quadrangle Region [93], five smart cities in India [60], Wuhan, China [94], and Kolkata [95] all found that ecological quality suffers from rapid urbanization but can improve with proper planning [86,95]. Remote sensing techniques were also successfully used to assess ecological quality [43].

An in-depth assessment of the ecological environment in Dhaka using RSEI and a set of many ecological indicators gives strong evidence for the significant degradation of the local ecology within the last three decades. The results are in good agreement with numerous studies from different regions, so they reinforce the validity of this study’s approach and results. The integration and application of PCA, spatial autocorrelation analysis, and correlation analysis in this study demonstrate strong potential for achieving this objective. The integration of these methods enables a comprehensive understanding of how ecological indicators interact dynamically with the impacts of urbanization and industrial development. The increasing low-quality areas and decreasing high-quality areas hint at the sustainable urban development practices and focused ecological management strategies required now. The relative consistencies of these patterns of environmental degradation due to urbanization and industrial activities, as observed in the current study, are well corroborated by other studies. Therefore, the current study’s findings agree with their global counterparts and underline the critical need for effective environmental management strategies. These strategies should focus on the mitigation of the adverse impacts of urbanization and industrialization to guarantee sustainable development and preservation of ecological quality in rapidly urbanizing regions like Dhaka, and this will guide policymakers, urban planners, and environmental managers in strategizing how to counteract ecological degradation. Integrated urban design plans are absolutely necessary to slow down more environmental damage. Preserving and restoring wetlands, implementing land-use zoning rules, improving urban greening projects, including rooftop gardens and urban parks, and using sustainable drainage systems (SuDS) should all take first priority. Furthermore, applying current frameworks such as the DAP, along with supporting community-based conservation initiatives, can help enhance Dhaka’s urban ecological resilience. However, several limitations of this study should be considered: the fact that most data originate from remote sensing might be an advantage in providing very broad temporal and spatial coverage but may also miss finer ecological details and local nuances that could be captured at the ground level. Some other important factors were neglected by this survey, including the most obvious ones like air and water pollution, biodiversity, and soil quality. A decadal temporal resolution may overlook short-term ecological variations and trends. In addition, it is specific to the context of Dhaka, which is informative but not fully representative of the conditions in other rapidly urbanizing areas.

5. Conclusions

The remote sensing-based ecological environment quality assessment (RSEI) results indicate a continuous degradation of Dhaka’s ecological environment over the past three decades. The analysis of Landsat imagery from 1993 to 2023 revealed a 39.3% increase in the areas classified as low- and very low-EEQ, while high- and very high-ecological quality areas decreased by 24% in Dhaka. PCA confirmed that all four components (LST, LSM, NDVI, and NDBSI) significantly contributed to the RSEI values. Among these, LST and NDBSI showed a strong negative correlation with RSEI, especially in 1993, while NDVI and LSM generally had a positive correlation, with a notable negative correlation in 1993. The increasing extent of bare soil and impervious surfaces contributes to ecological degradation and intensifies urban heat effects. About 58–66% of environmental disturbance is associated with the surface moisture, urban dryness, and vegetation cover. However, the environmental degradation is not spatially concentrated; it is rather scattered, as suggested by the L-H and H-L clusters. Industrial zones, in particular, exhibited severely degraded ecological conditions. To ensure urban ecological resilience, the promotion of sustainable urbanization practices, such as preserving green spaces and regulating land use, is essential. However, the analysis was constrained by limited spatial and temporal resolution, which may have affected the precision of the results. Incorporating additional parameters, such as air quality, socio-economic indicators, or hydrological data, in future analyses could enhance the robustness of ecological assessments. The findings of this study provide crucial spatial and temporal insights that can support policymakers and urban planners in formulating targeted strategies for improving Dhaka’s ecological sustainability.