Modified Normalized Difference Water Index Research Articles

Analysis of shoreline changes in the city of Gorontalo using remote sensing technology

Coastal areas are essential for economic activities but are also vulnerable to environmental changes caused by human activities. Remote sensing technology can help monitor shoreline changes efficiently and accurately related to the spatial-temporal dynamics of the local area's coastline, which is needed to develop an effective coastal management strategy. By using the Modified Normalized Difference Water Index (MNDWI) water index method for shoreline extraction, the results show that the addition of coastal areas (accretion) as part of the dominant process for coastal equilibrium in almost all coastal regions in Gorontalo City during the 2000-2022 period, experienced trend acceleration significantly during the 2015-2022 period with an increase in the accretion rate of 60.15%. The accretion rate in this period has resulted in an increase in the land area of the coastal zone by 425.44% compared to the accretion that occurred during the 2000-2015 period. The findings of this study can be used as a basis for further research regarding the impact of human activities on coastal ecosystems and the effectiveness of coastal management strategies for Gorontalo City.Keywords: Google earth engine; coastal equilibrium; landsat; sentinel-2; MNDWI

Comparison of Synthetic Aperture Radar Sentinel-1 and ALOS-2 observations for lake monitoring

This work investigates the efficacy of L-band and C-band Synthetic Aperture Radar (SAR) sensors onboard ALOS-2 and Sentinel-1 satellites, as compared to optical sensors onboard Sentinel-2 satellite, for mapping open water of the Tri An reservoir, one of the largest artificial reservoirs in South Vietnam, during the 2016-2023 period. The Google Earth Engine (GEE) was the primary computing platform to pre-process all satellite observations. The Otsu threshold algorithm was employed to generate water/non-water maps derived from the VH- and HH-polarized backscatter coefficient data acquired by Sentinel-1 and ALOS-2 satellites and from the Modified Normalized Difference Water Index (MNDWI) data acquired by Sentinel-2 satellite, respectively. The findings reveal the stability of Tri An reservoir’s surface water extent from 2017 to 2022, followed by a significant decline of nearly 70% during the dry season of 2023 to approximately 100 km2. This substantial decrease can be explained by the impact of a robust El Niño phase occurring in the region simultaneously. Overall, there is a high consistency between results derived from SAR and optical sensors, but the correlation between Sentinel-1 and Sentinel-2 (R = 0.9774) was higher than that between ALOS-2 and Sentinel-2 (R = 0.9145). During the drought period, both C-band and L-band SAR sensors overestimate the reservoir’s surface water extent due to the similarity in their backscatter coefficient between water and dry flat soil surfaces. This misclassification is more pronounced in ALOS-2 data than Sentinel-1 data, suggesting that the C-band sensor is more suitable than the L-band sensor for mapping the lake’s open water areas.

Spatio-temporal classification of land use and land cover and its changes in Kerala using remote sensing and machine learning approach.

Land use and land cover (LULC) analysis gives important information on how the region has evolved over time. Kerala, a land with an extensive and dynamic history of land-use changes, has, until now, lacked comprehensive investigations into this history. So the current study focuses on Kerala, one of the ecologically diverse states in India with complex topography, through Landsat images taken from 1990 to 2020 using two different machine learning classifications, random forest (RF) and classification and regression trees (CART) on Google Earth Engine (GEE) platform. RF and CART are versatile machine learning algorithms frequently employed for classification and regression, offering effective tools for predictive modelling across diverse domains due to their flexibility and data-handling capabilities. Normalised Difference Vegetation Index (NDVI), Normalised Differences Built-up Index (NDBI), Modified Normalised Difference Water Index (MNDWI), and Bare soil index (BSI) are integral indices utilised to enhance the precision of land use and land cover classification in satellite imagery, playing a crucial role by providing valuable insights into specific landscape attributes that may be challenging to identify using individual spectral bands alone. The results showed that the performance of RF is better than that of CART in all the years. Thus, RF algorithm outputs are used to infer the change in the LULC for three decades. The changes in the NDVI values point out the loss of vegetation for the urban area expansion during the study period. The increasing value of NDBI and BSI in the state indicates growth in high-density built-up areas and barren land. The slight reduction in the value of MNDWI indicates the shrinking water bodies in the state. The results of LULC showed the urban expansion (158.2%) and loss of agricultural area (15.52%) in the region during the study period. It was noted the area of the barren class, as well as the water class, decreased steadily from 1990 to 2020. The results of the current study will provide insight into the land-use planners, government, and non-governmental organizations (NGOs) for the necessary sustainable land-use practices.

Estimation and Evaluation of 15 Minute, 40 Meter Surface Upward Longwave Radiation Downscaled from the Geostationary FY-4B AGRI

Surface upward longwave radiation (SULR) is one of the four components of surface net radiation. Geostationary satellites can provide high temporal but coarse spatial resolution SULR products. Downscaling coarse SULR to a higher resolution is important for fine-scale thermal condition monitoring. Statistical regression downscaling is widely used due to its simplicity and is built on the assumption that the thermal parameter like land surface temperature (LST) or SULR has a relationship with the related surface factors like the normalized difference vegetation index (NDVI), and the relationship remains unchanged in any scales. In this study, to establish the relationship between SULR and the related surface factors, we chose the multiple linear regression (MLR) model and five surface factors (i.e., the modified normalized difference water index (MNDWI), normalized difference built-up and soil index (NDBSI), NDVI, normalized moisture difference index (NMDI), and urban index (UI)) to drive the downscaling process. Additionally, a step-by-step downscaling strategy was applied to reach the 100-fold increase in spatial resolution, transitioning the estimated SULR from 4 km of the advanced geostationary radiation imager (AGRI) onboard FengYun-4B (FY-4B) satellite to 40 m of the visual and infrared multispectral imager (VIMI) in infrared spectrum onboard GaoFen5-02 (GF5-02). Finally, we evaluated the downscaling results by comparing the downscaled SULR values with the in situ measured SULR and GF5-02-calculated SULR, and the root mean square errors (RMSEs) were 19.70 W/m2 and 24.86 W/m2, respectively. Throughout this MLR-based step-by-step downscaling method (high-frequency data from FY-4B and high spatial resolution data from GF5-02), high spatiotemporal SULR (15 min temporal resolution, 40 m spatial resolution) were successfully generated instead of coarse spatial resolution ones from the FY-4B satellite or a coarse temporal resolution one from the GF5-02 satellite, relieving the above-mentioned conflict to some extent.

Can land-use and land-cover change explain reduced resilience in forests?

Generating signals of reduced resilience in ecosystems is crucial for conservation and management endeavors. However, the practical implications of such systems are still limited due to the lack of high-frequency data and uncertainties associated with predicting complex systems such as ecosystems. This study aims to investigate the potential of time series analysis of remote sensing data in detecting signals of reduced resilience in mangrove forest ecosystems. Using time series analysis of remote sensing images, the resilience of mangrove forests was explored across two distinct study sites. One site (Qeshm Island) has been adversely affected by land-use and land-cover changes, while the other (Gabrik) serves as a reference ecosystem. The study uses data from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite to quantify three remotely sensed indices: the Normalized Difference Vegetation Index (NDVI), the Modified Normalized Difference Water Index (MNDWI), and the Modified Vegetation Water Ratio (MVWR). In addition, Landsat data has been used to explore temporal alterations in land-use and land-cover change. To identify signals of reduced resilience, trend analyses of indicators such as autocorrelation (acf (1)) and standard deviation (SD) are applied. The findings revealed a notable decrease in resilience, signaled by significant upward trends in NDVI statistical metrics for Qeshm Island (Kendall’s τ of acf (1): 0.50 and SD: 0.90), contrasting with the pattern observed in Gabrik (Kendall’s τ of acf (1): −0.19 and SD: −0.19). These results align with our expectations derived from previous studies. Despite MNDWI significantly indicating reduced resilience in Qeshm Island (Kendall’s τ of acf (1): 0.86 and SD: 0.90), it also signaled decreased resilience in Gabrik (Kendall’s τ of acf (1): 0.79 and SD: 0.90). Moreover, MVWR failed to indicate signals of reduced resilience in both sites, specifically in Qeshm (Kendall’s τ of acf (1): −0.10 and SD: −0.07) and in Gabrik (Kendall’s τ of acf (1): −0.72 and SD: −0.12). These findings may be explained through quantitative analyses of land-use and land-cover change. While Qeshm Island and Gabrik share similarities in climate, geography, and annual rainfall, the analysis of land-use and land-cover change revealed significant differences between the two study areas. Qeshm Island underwent drastic increases in the built-up class by a 64.40% change between 1996 and 2014, whereas the built-up class expanded modestly by a 4.04% change in the Gabrik site. This study contributes to advancing our understanding of ecosystem dynamics. The findings of this study can be integrated with ecosystem management tools to enhance the effectiveness of conservation efforts. This is the first report of the successful application of remote sensing in generating signals of reduced resilience within mangrove forests in the Middle East.

Urban engineering insights: Spatiotemporal analysis of land surface temperature and land use in urban landscape

In the field of urban environment engineering, understanding the relationship between land surface temperature (LST) and land use land cover (LULC) is essential in rapidly growing and climatically unstable urban landscapes such as Chengdu. It helps alleviate the magnitude and intensity of Urban Heat Islands (UHIs). Toward this aim, summer and winter Landsat images were acquired in four years from 1992 to 2021 and were used to extract LULC classes, LST and three indices of the Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), and Modified Normalized Difference Water Index (MNDWI) and analyze their spatiotemporal associations. Results showed that built-up areas expanded approximately six times (820.82 Km2, 584.96%) from 1992 to 2021. Meanwhile, their mean LST increased in both seasons, by 9.94 °C in summer and by 0.95 °C in winter. The LST-NDBI correlation was significant and positive in both the summer and winter of the studied years (0.437< r <0.874, p=0.00) while a very high variability was observed in the LST-NDVI (-0.835< r <0.255, p=0.00) and LST-MNDWI (-0.632< r <0.628, p=0.00) correlation coefficients. According to the results, NDBI can be used as a good intra- and inter-annual predictor of LST in Chengdu, especially in the context of its fast-paced physical expansion and increasing intensity of UHI.

Pre-disposed tectonic subsidence controls flood hazards and unplanned urbanisation dominates the flood disasters in the Pliocene to Holocene Kashmir basin, NW Himalayas

The Pliocene to Holocene Kashmir basin of NW Himalaya, India, is prone to various geological and climatic hazards routinely connected to the climatic and structural setting of the Himalayan intermontane basins. The topographic expression of the basin is a direct consequence of the active plate tectonic convergence between the lithosphere plates of India and Eurasia. However, the role of the tectonic framework and its contribution to flood hazards has remained an unresolved research question. Our previous work has contributed towards this problem, and here, we extend our previous work by producing robust evidence in support of the role of active faults and tectonic topography in shaping the flood hazards in the Kashmir basin and its implication for the other similar basins in the world. The Landsat 8 Operational Land Imager-Thermal Infrared Sensor (OLI-TIRS) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) images were used to extract pre-2014 floodwater bodies and 2014 flooded areas in the Jhelum River valley of the Kashmir region, respectively. Water pixels were extracted using the modified normalized difference water index (MNDWI) methods, and similarly, the flood depth and duration (in days) were computed. The flood depth was correlated with the Jhelum River valley's topography, morphology, and geology. The slope and topography were also associated with the 2014 flood disaster to map the influence of the pre-disposed structural setting of the basin on flood vulnerability. The results suggest that flood disasters were mainly related to the unplanned built area, which remains the dominant factor in how flood hazards have turned into disasters. The results reveal the tectonically derived structural configuration of the basin mainly contributed towards the flood hazards, which is the primary predisposed structural framework to dictate flood hazards since the formation of the basin ∼4.0 million years ago. Therefore, the primary outcome of our work is the strong evidence that flood hazards are related to the structural setting of the basin, which includes topography and geology, while the transition of flood hazards into disasters is mainly because of unplanned urbanization.

Assessing Satellite-Derived Shoreline Detection on a Mesotidal Dissipative Beach

The accuracy and robustness of the shoreline definition from satellite imagery on different coastal types are crucial to adequately characterising beach morphology and dynamics. However, the generic and widespread application of satellite-derived shoreline algorithms is limited by the lack of robust methods and parameter assessments. This work constitutes a quantitative and comprehensive assessment of the satellite-derived waterlines from Sentinel-2 by using the novel SAET tool (Shoreline Analysis and Extraction Tool) on the exposed and mesotidal beach of La Victoria (Cádiz, SW Spain). The diverse parameters available in SAET, such as water indexes, thresholding methods, morphological filters, and kernel sizes, were combined to define water/land interface positions that were compared against coincident video-derived waterlines. Satellite-derived waterline errors are found to be affected by extraction parameters, as well as by the oceanographic and morphological conditions at the time of the image acquisition. The application of a morphological erosion filter on the water mask, which tends to shift the extracted waterline seawards and reduce bias, is the best solution at the dissipative site of La Victoria Beach. Moreover, using a 3 × 3 kernel size consistently shows higher accuracies than a larger kernel. Although there was no parameter combination showing the best skill for all dates, the employment of the Automated Water Extraction Index for images with no shadows (AWEInsh) with a threshold = 0, erosion morphological filter, and 3 × 3 kernel was, overall, the best combination of extraction parameters for this beach (average waterline RMSE of 5.96 m). The combination of the Modified Normalised Difference Water Index (MDNWI) with the Otsu thresholding also led to similar positions of the resulting waterlines and offered good accuracies. In line with other recent research efforts, our work stresses the lack of generic shoreline extraction solutions that can be applied automatically at a global level and the necessity to adapt and validate the extraction methodologies to the different types of coastlines.

Accuracy Assessment of Land Use/Land Cover Indices for Al-Rusafa in Baghdad Governorate by Remote Sensing Technology and GIS

The management and planning of natural and artificial resources depend on accurately monitoring land cover changes. Land cover change mapping and monitoring used to require expensive field surveys. Remote sensing is cheaper and more practical for mapping land use and cover changes. The Tigris River divides the Iraqi capital, Baghdad, into two parts: Karkh and Rusafa. Al-Rusafa was selected as a study area for current research, which has had rapid population and urban growth in recent decades. The current research applies the support vector machine technique to supervised LU/LC maps’ classification into barren regions, water bodies, vegetation cover and built-up regions. Spectral indicators were calculated: Enhanced Vegetation Index, Modified Normalized Difference Water Index, Normalized Built-Up Area Index, Dry Bareness Index in addition to calculating the accuracy assessment and Kappa coefficient. Using the Landsat 9 satellite image, ArcGIS 10.8 and Envi5.3 software were used to analyze and evaluate the results and field points observed by GPS devices. The results showed that the SVM classification algorithm accurately revealed the categories of LU/LC, where the classification accuracy reached 95%, and that the arid lands covered most of the study area 848.864 km2 and water bodies 76.747 km2, the vegetation and the built-up regions 466.459 km2 and 439.077 km2, respectively. The spectral indices showed slightly different areas of barren lands (DBSI 752.589 km2, 93% accuracy), vegetation (EVI 423.651 km2, 96% accuracy), and water bodies (MNDWI 73.187 km2, 98% accuracy) and built-up areas (NBAI 501,731 km2, 90%accuracy). The Support Vector Machine method outperforms other classification methods, and the spectral indicators employed in this work are useful and dependable for extracting each LU/LC category. In conclusion, Landsat 9 satellite data can reliably and swiftly detect ground cover.

A comprehensive analysis of surface water changes in Dau Tieng Lake, Vietnam, across dry and wet seasons

This study investigates the seasonal dynamics of Dau Tieng Lake near Tay Ninh, Vietnam, employing remote sensing technology, specifically the Modified Normalized Difference Water Index (MNDWI) and Normalized Difference Vegetation Index (NDVI). Focused on the period from 2016 to 2020, the research aims to detect changes in the water surface area during dry and wet seasons. As the largest reservoir for water supply and irrigation in Vietnam, Dau Tieng Lake faces operational challenges due to seasonal rainfall variations and increasing water demands. The results reveal significant differences in water body areas between wet and dry seasons, with MNDWI proving more effective in capturing these variations compared to NDVI. The study concludes that the seasonal change in Dau Tieng Lake is 21.76 km2 (NDVI) and 25.55 km2 (MNDWI), emphasizing the latter's superiority in accurately detecting water body changes. These findings contribute valuable insights for improved reservoir management and sustainable utilization of water resources in the region.

Monitoring Changes in the Jakarta Flood Disaster with Using Sentinel 2A Imagery

DKI Jakarta Province is one of the provinces in Indonesia with a relatively high incidence of flooding since its establishment in the Dutch East Indies era because geomorphologically Jakarta is a floodplain area. On the other hand, land pressure by increasing urbanization in the city of Jakarta results in the increasingly limited availability of decent housing land for the community so that lands that should be water catchment areas and riverbanks are transformed into densely populated flying areas which ultimately reduces water absorption capacity in the Jakarta area. To obtain fast data in identifying flood-affected areas, the technique of identifying the potential extent and distribution of flooding by utilizing the remote sensing NDWI (Normalized Difference Water Index) method and/or using the Modified Normalized Difference Water Index (MNDWI) method from the free data Sentinel 2A satellite can be useful to provide data input to relevant parties in DKI Jakarta Province.  Opportunities and constraints The application of NDWI and/or MNDWI techniques will greatly assist mapping activities and identification of the distribution of flood areas as an alternative input to policymakers for action and anticipation of areas with high flood potential.  This research is one of the first steps and input to the DKI Jakarta Government in making strategies and roadmaps for flood mitigation in DKI Jakarta.

Cost-Effective Groundwater Potential Mapping by Integrating Multiple Remote Sensing Data and the Index–Overlay Method

The Choushui River groundwater basin (CRGB) in Yunlin County, Taiwan, is a significant groundwater source for the western part of the region. However, increasing groundwater demand and human activities have triggered a potential crisis due to overexploitation. Therefore, groundwater potential zone (GWPZ) maps are crucial for mapping groundwater resources and water resource management. This study employs the normalized index–overlay method and fuzzy extended analytical hierarchy process (FE-AHP) to map GWPZs cost-effectively. The methodology objectively incorporates weightings from various thematic layers by normalizing and correlating parameters with observed groundwater availability (GA). Site-specific observations, including aquifer thickness, depth to the groundwater level, and porosity, inform GA calculations. Seven comprehensive layers derived from remote sensing (RS) data are processed to obtain weightings and ratings for the groundwater potential index (GWPI) in the CRGB. Selected parameters are categorized into hydrological processes, human interventions, geological, and surface profiles. Hydrological processes include precipitation, modified normalized difference water index (MNDWI), and drainage density. Human interventions consist of the enhanced vegetation index (EVI) and normalized difference building index (NDBI). Surface profiles encompass the terrain ruggedness index (TRI) and slope, enhancing the study’s multi-criteria approach. The observed GA validates the GWPZ accuracy, classifying zones into five categories. According to the GWPI of FE-AHP, about 59.56% of the CRGB area can be categorized as “moderate” to “very good” potential groundwater recharge zones. Pearson’s correlation coefficient between GWPI and GA, based on FE-AHP, outperforms the conventional AHP. This RS-based approach efficiently evaluates GA in aquifers with limited wells, highlighting crucial zones in CRGB’s proximal-fan and southeastern mid-fan for informed groundwater management strategies.

Geo-ecological, shoreline dynamic, and flooding impacts of Cyclonic Storm Mocha: A geospatial analysis

This research comprehensively assesses the aftermath of Cyclonic Storm Mocha, focusing on the coastal zones of Rakhine State and the Chittagong Division, spanning Myanmar and Bangladesh. The investigation emphasizes the impacts on coastal ecology, shoreline dynamics, flooding patterns, and meteorological variations. Employed were multiple vegetation indices—Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Modified Vegetation Condition Index (mVCI), Disaster Vegetation Damage Index (DVDI), and Fractional Vegetation Cover (FVC)—to evaluate ecological consequences. The Digital Shoreline Assessment System (DSAS) aided in determining shoreline alterations pre- and post-cyclone. Soil exposure and flood extents were scrutinized using the Bare Soil Index (BSI) and Modified Normalized Difference Water Index (MNDWI), respectively. Additionally, the study encompassed an analysis of microclimatic variables, comparing meteorological data across pre- and post-cyclone periods. Findings indicate significant ecological impacts: an estimated 8985.46 km2 of dense vegetation (NDVI >0.6) was adversely affected. Post-cyclone, there was a discernible reduction in EVI values. The mean mVCI shifted negatively from −0.18 to −0.33, and the mean FVC decreased from 0.39 to 0.33. The DVDI underscored considerable vegetation damage in various areas, underscoring the cyclone's extensive impact. Meteorological analysis revealed a 245 % increase in rainfall (20.22 mm on May 14, 2023 compared to the May average of 5.86 mm), and significant increases in relative humidity (14 %) and wind speed (205 %). Erosion was observed along 74.60 % of the studied shoreline. These insights are pivotal for developing comprehensive strategies aimed at the rehabilitation and conservation of critical coastal ecosystems. They provide vital data for emergency response initiatives and offer resources for entities engaged in enhancing coastal resilience and protecting local community livelihoods.

Mapping and Assessing Riparian Vegetation Response to Drought along the Buffalo River Catchment in the Eastern Cape Province, South Africa

The increasing drought frequency poses a significant threat to global and regional river systems and ecosystem functioning, especially in the complex topographical Buffalo River catchment area of the Eastern Cape Province, South Africa. This study explored the impact of drought on riparian vegetation dynamics using the Normalize Difference Vegetation Index (NDVI), Transformed Difference Vegetation Index (TDVI) and Modified Normalized Difference Water Index (MNDWI) from satellite-derived Landsat data from 1990 to 2020. The least-squares linear regression and Pearson’s correlation coefficient were used to evaluate the long-term drought in riparian vegetation cover and the role of precipitation and streamflow. The correlation results revealed a moderate positive correlation (r = 0.77) between precipitation and streamflow with a significant p-value of 0.04 suggesting consequences on riparian vegetation health. Concurrent with the precipitation, the vegetation trends showed that precipitation increased insignificantly with less of an influence while the reverse was the case with the streamflow in the long term. The results show that the NDVI and TDVI were significant indices for detecting water-stressed vegetation in river catchment dynamics. Much of these changes were reflected for MNDWI in dry areas with a higher accuracy (87.47%) and dense vegetation in the upper catchment areas. The standardized precipitation index (SPI) revealed the inter-annual and inter-seasonal variations in drought-stressed years between 1991–1996, 2000–2004, 2009–2010, 2015, and 2018–2019, while 2020 exhibited slight sensitivity to drought. The findings of this study underscore the need for heightened efforts on catchment-scale drought awareness for policy development, programs, and practices towards ecosystem-based adaptation.

Early Mapping Method for Different Planting Types of Rice Based on Planet and Sentinel-2 Satellite Images

In Northeast China, transplanted rice cultivation has been adopted to extend the rice growing season and boost yields, responding to the limitations of the cumulative temperature zone and high food demand. However, direct-seeded rice offers advantages in water conservation and labour efficiency. The precise and timely monitoring of the distribution of different rice planting types is key to ensuring food security and promoting sustainable regional development. This study explores the feasibility of mapping various rice planting types using only early-stage satellite data from the rice growing season. We focused on Daxing Farm in Fujin City, Jiamusi City, Heilongjiang Province, for cropland plot extraction using Planet satellite imagery. Utilizing Sentinel-2 satellite imagery, we analysed the differences in rice’s modified normalized difference water index (MNDWI) during specific phenological periods. A multitemporal Gaussian mixture model (GMM) was developed, integrated with the maximum expectation algorithm, to produce binarized classification outcomes. These results were employed to detect surface changes and map the corresponding rice cultivation types. The probability of various rice cultivation types within arable plots was quantified, yielding a plot-level rice-cultivation-type mapping product. The mapping achieved an overall accuracy of 91.46% in classifying rice planting types, with a Kappa coefficient of 0.89. The area extraction based on arable land parcels showed a higher R2 by 0.1109 compared to pixel-based area extraction and a lower RMSE by 0.468, indicating more accurate results aligned with real statistics and surveys, thus validating our study’s method. This approach, not requiring labelled samples or many predefined parameters, offers a new method for rapid and feasible mapping, especially suitable for direct-seeded rice areas in Northeast China. It fills the gap in mapping rice distribution for different planting types, supporting water management in rice fields and policies for planting-method changes.

Analysis of coastline extraction indices using Sentinel-2 and Google Earth Engine, case study in Bali, Indonesia

Coastal zone is the most dynamic environment and requires regular monitoring to maintain sustainable coastal resource management. Employing remote sensing technology to monitor changes involves an essential process referred to as coastline extraction. This study aims to conduct a comparative analysis of five commonly employed techniques for coastline extraction: Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), Water Ratio Index (WRI), and Automated Water Extraction Index (AWEI). These methods are evaluated by automatically delineating sandy beaches and coastlines using highresolution imagery from the Sentinel-2 satellite for Bali, Indonesia. The Otsu algorithm is utilized to determine the optimal threshold value. Compared to the other indices, the results indicate that MNDWI proved to be the most effective in highlighting water bodies, with the average distance from the validated point of MNDWI being 12.9 m and a Mean Absolute Error (MAE) of 7.11, whereas NDVI demonstrated a high level of proficiency in detecting coastal vegetation. This study highlights the potential of utilizing both Sentinel-2 satellite imagery and the Google Earth Engine (GEE) platform for efficient coastline monitoring. This study also provides scientific evidence supporting the reliability and accuracy of coastline extraction through spectral water indices.

An analysis of LULC changes for understanding the impact of anthropogenic activitieson food security: a case study of Dudhganga watershed, India.

Although the Dudhganga watershed is the primary water and food resource of the Kashmir Valley, it has undergone significant changes in food resources and strategies due to rampant urbanization in the area over the past 20years. This urbanization has had a profound impact on the watershed and has also affected land use and land cover (LULC) patterns and environmental changes. The objective of this study is to investigate the effects of urban development on food security parameters in the Dudhganga watershed area, India, from 2000 to 2020, by evaluating LULC changes. Additionally, the study aims to examine the relationship between climate changes and LULC indices, such as the Modified Normalized Difference Water Index (MNDWI), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Built-up Index (NDBI). The results indicate a 21.66% increase in barren areas, at the expense of snow-covered lands, during the 2000-2020 period. The primary land cover transition observed is towards barren areas. The predictions for LULC in 2030 highlight the need for careful management of land use and climate changes in the study area. This study can assist local government officials in reassessing food strategies by identifying areas where urban expansion should be controlled and climate impacts minimized, to prevent local hunger and ecological degradation. Therefore, the development of systematic urban planning approaches and mitigation of climate change sources are crucial. Furthermore, the adoption of advanced agricultural technology should be considered to mitigate the impact of urban expansion.

Can Water-Detection Indices Be Reliable Proxies for Water Discharges in Mid-Sized Braided Rivers Using Coarse-Resolution Landsat Archives?

The use of water detection (WD) indices to infer daily discharge (Qd) has a great potential to enrich needed hydrological data for understanding fluvial processes driving the morphological changes of braided rivers. However, no consensus has been reached on which one stands out for use in mid-sized braided rivers. In this study, we compared the physical characteristics of three most commonly used WD indices, the Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), and Normalized Difference Moisture Index (NDMI), for two mid-sized braided reach segments in the Qinghai-Tibet Plateau, China, that have different morphological structures. Relying on the Google Earth Engine web interface, we calculated the total mean water width (WWt) based on the detected surface-water areas (As) and braiding index (BI), as well as the mean values (m) of these indices over about four decades at the braided corridor scale (cs) (mNDWIcs, mMNDWIcs, and mNDMIcs). We then examined different responses of these indices to water and non-water features and their best threshold values for characterizing channel structures. Our analyses demonstrated that (1) NDWI and MNDWI perform well for detecting braided channel structures with the threshold of zero; (2) WWt is generally better correlated to Qd in a linear style than WD indices do, particularly when calculated from MNDWI; and (3) among WD indices calculated at the braided corridor scale, mMNDWIcs shows a better relationship with Qd than mNDMIcs does. Finally, we provided mechanisms that may explain these differences in terms of photometric discrepancies in calculating WWt and WD indices and the impact of image resolution on their calculations.

Studying the spatial non-stationary relationships of some physical parameters on the Earth's surface temperature using GWR in Upper Awash basin, Ethiopia

Exploring the spatial non-stationary relationships between land surface temperature (LST) and their driving environmental factors is important for selecting appropriate strategies to mitigate and regulate the thermal environment of watersheds. To examine the influence of various biophysical factors on LST in the Upper Awash Basin (UAB) of Ethiopia, the study used two models: Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) models. As a global model, the OLS model was initially used to capture the overall relationship between LST and some biophysical factors. And then the GWR, a local spatial modeling approach, was used to examine the spatial non-stationary relationships between LST and its influencing biophysical factors. Landsat 8 OLI/TIRS image and Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) were used to generate the LST and its influencing biophysical factors. Biophysical parameters such as enhanced vegetation index (EVI), modified normalized difference water index (MNDWI), normalized difference built-up index (NDBI), normalized difference bareness index (NDBaI), albedo and elevation were used as potential driving environmental factors of LST. The result showed that the GWR model, with a higher coefficient of determination (R2) (GWR: 0.98; OLS: 0.52) and a smaller Akaike Information Criterion (AIC) (GWR: 12354; OLS: 65412), provides a better prediction than the traditional OLS model, reflecting the spatial non-stationarity relationships. The results also showed that increased LST was significantly affected by NDBI, NDBaI, and albedo, with NDBI having the greatest effect. Conversely, EVI, MNDVI, and DEM showed a negative correlation with LST, with EVI having the greatest impact. These findings highlighted the importance of considering the spatial non-stationarity relationships between LST and pertinent driving factors, and they also offer recommendations for mitigating measures to control the thermal environment of a river basin.

THE EFFECTS OF LAND COVER CHANGES ON LAND SURFACE TEMPERATURES

Abstract. The aims of this study are to detect the land-cover maps and land surface temperatures using Landsat time series and analyse the relation between the land-cover and land surface temperatures (LST) and their changes in time. For these purposes initially, land-cover maps were generated rapidly using land cover indices and automatic thresholding. The land-cover indices used in this study are Normalized Difference Vegetation Index (NDVI), Modified Normalized Difference Water Index (MNDWI), Index-Based Built-Up Index (IBI), Modified Bare-Soil Index (MBI), Plastic-Mulched Landcover Index (PMLI), Plastic Greenhouse Index (PGI) and Normalized Burn Ratio Thermal (NBRT) Index. Then, using the thermal bands of Landsat satellites, LST maps were created. Finally, the land-cover and LST changes were examined. The Kumluca district of Antalya, which includes extensive greenhouse areas as well as urban, vegetation, bareland, water, was selected as study area. Between the years 2004 and 2021, within the study area the greenhouse areas increased significantly, the urban area expanded and some areas exposed to fire, especially the fire in 2016. Therefore, the images within this time period were used. The overall accuracies for land-cover maps were computed as 76%, 79%, 79%, 89% and 86% for the years 2004, 2009, 2013, 2017 and 2021, respectively. The results obtained from the study reveal that while greenhouse and urban areas were increased, the vegetation areas were decreased significantly within this time period. In addition, generally increases were observed for LST values of all land-cover classes and the highest LST values were detected for the burned, bareland, urban and greenhouse areas.