Evolution of the Porong River Estuary, Indonesia: Morphological Changes of Lusi Island through Sediment Modeling and Time-Series Interpretation of MNDWI

The accumulation of sediment in reservoirs poses a major challenge that impacts the storage capacity, quality of water, and efficiency of hydroelectric power generation systems. Geospatial methods, including Geographic Information Systems (GIS) and Remote Sensing (RS), were used to assess Dukan Reservoir sediment quantities. Satellite and reservoir water level data from 2010 to 2022 were used for sedimentation assessment. The satellite data was used to analyze the water spread area, employing the Normalized Difference Water Index (NDWI) and Modified Normalized Difference Water Index (MNDWI) to enhance the water surface in the satellite imagery of Dukan Reservoir. The cone formula was employed to calculate the live storage capacity of the reservoir within two elevations. According to the study results, the live storage capacity of Dukan Reservoir at elevation 511.78 m had decreased from 8000 MCM to 7007.77MCM and 6923.53 MCM using NDWI and MNDWI respectively, due to sedimentation, resulting in a capacity loss of 14.59% and 15.83% for NDWI and MNDWI respectively. The annual sedimentation was 13.78 MCM and 14.95 MCM for NDWI and MNDWI, respectively. Joglekar's equation and Khosla's formula have demonstrated that the sedimentation rate in the Dukan reservoir exceeds the critical rate. The findings of this study will inform the development of sediment management strategies aimed at preserving the reservoir's capacity.

Characterization of water status and vegetation cover change in a watershed in Northeastern Brazil

Extraction of water bodies from satellite imagery has been broadly explored in the current decade. So many techniques were involved in detecting of the surface water bodies from satellite data. To detect and extracting of surface water body changes in Nagarjuna Sagar Reservoir, Andhra Pradesh from the period 1989 to 2017, were calculated using Landsat-5 TM, and Landsat-8 OLI data. Unsupervised classification and spectral water indexing methods, including the Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI), Normalized Difference Water Index (NDWI), and Modified Normalized Difference Water Index (MNDWI), were used to detect and extraction of the surface water body from satellite data. Instead of all index methods, the MNDWI was performed better results. The Reservoir water area was extracted using spectral water indexing methods (NDVI, NDWI, MNDWI, and NDMI) in 1989, 1997, 2007, and 2017. The shoreline shrunk in the twenty-eight-year duration of images. The Reservoir Nagarjuna Sagar lost nearly around one-fourth of its surface water area compared to 1989. However, the Reservoir has a critical position in recent years due to changes in surface water and getting higher mud and sand. Maximum water surface area of the Reservoir will lose if such decreasing tendency follows continuously.

Many techniques are available for detection of shorelines from multispectral satellite imagery, but the choice of a certain technique for a particular study area can be tough. Hence, for the first time in literature, an inter-comparison of the most widely used shoreline mapping techniques such as Normalized Difference Water Index (NDWI), Modified NDWI (MNDWI), Improved Band Ratio (IBR) Method, and Automatic Water Extraction Index (AWEI) has been done along four different coastal stretches of India using multitemporal Landsat data. The obtained results have been validated with the high-resolution images of Cartosat-2 (panchromatic) and multispectral images from Google Earth. Performance of the above indices has been analyzed based on the statistics, such as overall accuracy, kappa coefficient, user's accuracy, producer's accuracy, and the average deviation from the reference line. It is observed that the performance of NDWI and IBR techniques are dependent on the physical characteristics of the sites, and therefore, it varies from one site to another. Results indicate that unlike these two indices, the AWEI algorithm performs consistently well followed by MNDWI irrespective of the land cover types.

This study is aimed to analyze the dynamics of land use/ land cover (LU/LC) change in a newly created special economic zone, Gautam Buddha Nagar district during 2003–2015. The Landsat satellite data has been used to map the LU/LC pattern of 2003 and 2015 of the study area, using indices overlay method. Consequently, the indices overlay have been created using three land-use indices, i.e. modified normalized difference water index (MNDWI), soil adjusted vegetation index (SAVI), and enhanced built-up and bareness index (EBBI), and then the maximum likelihood classifier (MLC) has been used for the LU/LC classification. The result illustrates that the built-up area (419.35%) and open land (388.36%) have increased during 2003–2015 while the cropland (− 34.38%), scrubland (− 73.25%), and water bodies (− 58.37%) have declined. Further, northern parts of the district have experienced maximum change in the LU/LC while the southern parts have experienced comparatively low change. The study also reveals that the increase in the built-up area occurred mostly at the cost of cropland and scrubland. The statistical analysis shows that the EBBI and SAVI have high relationships with LU/LC while the MNDWI has a comparatively low relationship. The study concludes that cropland and scrubland are the main LU/LC types that get transformed due into the built-up area the study area and the SAVI, MNDWI, and EBBI are the good indicators in the study of LU/LC classification and change analysis.

Temporal geomorphic modifications and climate change impacts on the lower course of the São Francisco River, Brazil

With rapid urbanization, many cities have experienced significant changes in land use and land cover (LULC), triggered urban heat islands (UHI), and increased the health risks of citizens’ exposure to UHI. Some studies have recognized residents’ inequitable exposure to UHI intensity. However, few have discussed the spatiotemporal heterogeneity in environmental justice and countermeasures for mitigating the inequalities. This study proposed a novel framework that integrates the population-weighted exposure model for assessing adjusted thermal comfort exposure (TCEa) and the spatiotemporal weighted regression (STWR) model for analyzing countermeasures. This framework can facilitate capturing the spatiotemporal heterogeneities in the response of TCEa to three specified land-surface and built-environment parameters (i.e., enhanced vegetation index (EVI), normalized difference built-up index (NDBI), and modified normalized difference water index (MNDWI)). Using this framework, we conducted an empirical study in the urban area of Fuzhou, China. Results showed that high TCEa was mainly concentrated in locations with dense populations and industrial regions. Although the TCEa’s responses to various land-surface and built-environment parameters differed at locations over time, the TCEa illustrated overall negative correlations with EVI and MNDWI while positive correlations with NDBI. Many exciting spatial details can be detected from the generated coefficient surfaces: (1) The influences of NDBI on TCEa may be magnified, especially in rapidly urbanizing areas. Still, they diminish to some extent, which may be related to the reduction in building construction activities caused by the COVID-19 epidemic and the gradual improvement of urbanization. (2) The influences of EVI on TCEa decline, which may be correlated with the population increase. (3) Compared with NDBI, the MNDWI had more continuous and stable significant cooling effects on TCEa. Several mitigation strategies based on the spatiotemporal heterogeneous relationships also emanated. The effectiveness of the presented framework was verified. It can help analysts effectively evaluate local thermal comfort exposure inequality and prompt timely mitigation efforts.

Climate variability and recurrent droughts have caused remarkable strain on water resources in most regions across the globe, with the arid and semi-arid areas being the hardest hit. The impacts have been notable on surface water resources, which are already under threat from massive abstractions due to increased demand, as well as poor conservation and unsustainable land management practices. Drought and climate variability, as well as their associated impacts on water resources, have gained increased attention in recent decades as nations seek to enhance mitigation and adaptation mechanisms. Although the use of satellite technologies has, of late, gained prominence in generating timely and spatially explicit information on drought and climate variability impacts across different regions, they are somewhat hampered by difficulties in detecting drought evolution due to its complex nature, varying scales, the magnitude of its occurrence, and inherent data gaps. Currently, a number of studies have been conducted to monitor and assess the impacts of climate variability and droughts on water resources in sub-Saharan Africa using different remotely sensed and in-situ datasets. This study therefore provides a detailed overview of the progress made in tracking droughts using remote sensing, including its relevance in monitoring climate variability and hydrological drought impacts on surface water resources in sub-Saharan Africa. The paper further discusses traditional and remote sensing methods of monitoring climate variability, hydrological drought, and water resources, tracking their application and key challenges, with a particular emphasis on sub-Saharan Africa. Additionally, characteristics and limitations of various remote sensors, as well as drought and surface water indices, namely, the Standardized Precipitation Index (SPI), Palmer Drought Severity Index (PDSI), Normalized Difference Vegetation (NDVI), Vegetation Condition Index (VCI), and Water Requirement Satisfaction Index (WRSI), Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), Land Surface Water Index (LSWI+5), Modified Normalized Difference Water Index (MNDWI+5), Automated Water Extraction Index (shadow) (AWEIsh), and Automated Water Extraction Index (non-shadow) (AWEInsh), and their relevance in climate variability and drought monitoring are discussed. Additionally, key scientific research strides and knowledge gaps for further investigations are highlighted. While progress has been made in advancing the application of remote sensing in water resources, this review indicates the need for further studies on assessing drought and climate variability impacts on water resources, especially in the context of climate change and increased water demand. The results from this study suggests that Landsat-8 and Sentinel-2 satellite data are likely to be best suited to monitor climate variability, hydrological drought, and surface water bodies, due to their availability at relatively low cost, impressive spectral, spatial, and temporal characteristics. The most effective drought and water indices are SPI, PDSI, NDVI, VCI, NDWI, MNDWI, MNDWI+5, AWEIsh, and AWEInsh. Overall, the findings of this study emphasize the increasing role and potential of remote sensing in generating spatially explicit information on drought and climate variability impacts on surface water resources. However, there is a need for future studies to consider spatial data integration techniques, radar data, precipitation, cloud computing, and machine learning or artificial intelligence (AI) techniques to improve on understanding climate and drought impacts on water resources across various scales.

Morphometric characterization and land use of the Pajeú river basin in the Brazilian semi-arid region

Floods are hazard which poses immense threat to life and property. Identifying flood-prone areas, will enhance flood mitigation and proper land use planning of affected areas. However, lack of resources, the sizable extent of rural settlements, and the evolving complexities of contemporary flood models have hindered flood hazard mapping of the rural areas in Ghana. This study used supervised Random Forest (RF) classification, Landsat 8 OLI, and Landsat 7 ETM + images to produce flood prone, Land Use Land Cover (LULC), and flood hazard maps of the Nasia Watershed in Ghana. The results indicated that about 418.82 km2 area of the watershed is flooded every 2–3 years (normal flooding) and about 689.61 km2 is flooded every 7–10 years (extreme flooding). The LULC classification produced an overall accuracy of 92.31% and kappa of 0.9. The flood hazard map indicated that land areas within hazard zones of the river include the Nasia community, Flood Recession Agricultural (FRA), rainfed and woodlands. When compared with a Modified Normalized Difference Water Index (MNDWI), the RF supervised classification had an edge over the MNDWI in estimating the flooded areas. The results from this study can be used by local administrators, national flood disaster management and researchers for flood mitigation and land use planning within the watershed.

A GIS and remote sensing approach for measuring summer-winter variation of land use and land cover indices and surface temperature in Dhaka district, Bangladesh

Land use changes and its impact on biophysical environment: Study on a river bank

Mapping rice crop using sentinels (1 SAR and 2 MSI) images in tropical area: A case study in Fogera wereda, Ethiopia

The land transformation in Kolkata city and its suburban area is mainly due to intensive population pressure and rapid urban sprawling. Consequently, the land surface temperature (LST) is continuously increasing and gradually intensifying the effects of the urban heat island. The aim of this study is to assess the spatiotemporal variation of LST in response to land use land cover change (LULC) during 1995–2020. The maximum likelihood classifier was used for the supervised classification of LULC and the accuracy assessment was done using the confusion matrix. Quin’s Mono-window algorithms for Landsat TM data of 1995 and 2010 and split-window algorithms for Landsat 8 OLI data of 2020 were applied to retrieve LST. Several spectral indices such as Normalized difference built-up index (NDBI), Normalized difference vegetation index (NDVI), and Modified normalized difference water index (MNDWI) were calibrated and pixel-specific overlay analysis was done for correlation study between spectral indices and LST. This work revealed that the rapid urban sprawling causes massive land transformation in the study area. The land conversions from trees outside forests (TOF) and agricultural land to built-up were significantly contributing to an overall increase in the mean LST during 1995–2020. The mean LST was comparatively high over Kolkata city than its suburban area. During 1995–2020, the mean LST was increased by nearly 8.43°C in the summer season and 4.32°C in the winter season. The increasing rate of LST was found relatively high over the built-up (7.06°C), agricultural land without crop (5.55°C), and open land (5.54°C). However, it was comparatively low over TOF (4.66°C) and water bodies (3.68°C). The LST was positively correlated to NDBI and negatively correlated to NDVI and MNDWI. In order to combat urban warming, this study will promote green city initiatives through sustainable land use planning.

Surface water/ice dynamic monitoring is crucial for many purposes, such as water resource management, agriculture, climate change, drought, and flood forecasting. New advances in remote sensing satellite data have made it possible to monitor the surface water/ice dynamics both spatially and temporally. However, there are many challenges when using these data, such as the availability of valid imagery, cloud contamination issues for Landsat-8, and sensitivity of Sentinel-1 C-band to wind speed, topography, and others. A combined methodology using Landsat-8 and Sentinel-1 Synthetic Aperture Radar (SAR) data was proposed to create monthly change maps at 30 m spatial resolution for the Lesser Slave Lake in Alberta, Canada, for the period 2017-2020. The potentials of multi-spectral indices for Landsat-8, such as the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Modified NDWI (MNDWI) as well as the Sentinel-1 SAR backscattering coefficients (VV-VH) and Normalized Difference Polarized Index (NDPI) for separating water/ice from the land were investigated. The results obtained from satellite data with historical discharge and water level measurements for the lake were compared. Furthermore, the results show that the MNDWI and VH are the most effective indices for creating the change maps. The overall accuracies achieved for MNDWI and VH are 92.10% and 68.86% for cold months and 99.88% and 98.49% for warm months, respectively.