Normalized Difference Water Index Research Articles

Spatial distribution and environmental correlations of Culex pipiens pallens (Diptera: Culicidae)in Haidian district, Beijing.

Culex pipiens pallens Coquillett, 1898(Diptera: Culicidae)was the dominant health threat to mosquito species in Beijing, and it is important to unravel the spatial distribution and environmental correlations of Cx. pipiens pallens in Beijing. 3S technology methods and spatial statistics were used to clarify the distribution pattern. Subsequently, linear and spatial regression were performed to detect the environmental factors linked with the density of Cx. pipiens pallens. The same "middle peak" spatial distribution pattern was observed for Cx. pipiens pallens density at the community, subdistrict, and loop area levels in our study area. In addition, there were various correlated environmental factors at the community and subdistrict scales. At the community scale, the summary values of the Modified Normalized Difference Water Index (MNDWI) in 2 km buffer zone (MNDWI_2K) were negatively correlated, and the summary values of Normalized Difference Built-up Index (NDBI) in 800 m buffer zone (NDBI_800) was positively correlated to the Cx. pipiens pallens density. However, the summary values of Normalized Difference Vegetation Index and Nighttime Light Index significantly affected Cx. pipiens pallens density at the subdistrict scale. Our findings provide insight into the spatial distribution pattern of Cx. pipiens pallens density and its associated environmental risk factors at different spatial scales in the Haidian district of Beijing for the first time. The results could be used to predict the Cx. pipiens pallens density as well as the risk of lymphatic filariasis (LF) infection, which would help implement prevention and control measures to prevent future risks of biting and LF transmission in Beijing.

Shoreline Extraction Methods from Sentinel-2 and PlanetScope Images

Abstract. This work aims to compare and assess the performance of certain methodologies for shoreline mapping based on the use of medium (10 m) and high resolution (3 m) multi-spectral imagery, provided by Sentinel-2 (S2) and PlanetScope (PS), respectively. Being Sentinel-2 part of the Copernicus missions, its data are freely available. PS imagery are also freely available for scientific research, upon approval by the European Space Agency of a related project proposal. Several spectral indices, including Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), Automated Water Extraction Index (AWEI), and Water Index (WI), were used for shoreline detection. In particular, two unsupervised classification techniques, the Gaussian Mixture Model (GMM) and K-means clustering were deployed as shoreline extraction methods. The outcomes of such approaches were validated using reference shorelines derived from aerial orthomosaics, generated from images acquired as close as possible to the satellite imagery dates, and the ”baseline and transect” approach for accuracy verification. Three tide-less Mediterranean beaches were used as study cases for comparison: the beach between Castelldefels and Gava in Spain, Feniglia and Marina di Grosseto in Italy. The results demonstrated sub-pixel accuracy in shoreline extraction, with Mean Absolute Distances ranging from 2 m to 5 m for S2 data and 1.5 m to 2 m for PS data. These findings highlight the potential of freely available satellite data for semi-automatic shoreline detection. Results obtained by using the combination of different indices and methodologies show that the best option may change depending on the considered context, hence future investigations should be dedicated to the development of a procedure for automatically determining the context-based (close to) optimal index-classifier combination.

Machine Learning-Based Fine Classification of Agricultural Crops in the Cross-Border Basin of the Heilongjiang River between China and Russia

The transboundary region along the Heilongjiang River, encompassing the Russian Far East and Northeast China, possesses abundant agricultural natural resources crucial for global food security. In the face of the challenge of disruptions in the global food supply chain, the precise monitoring and exploitation of agricultural resources in the Heilongjiang Basin becomes imperative. This study employed deep learning to classify crop status in 2023 in the Heilongjiang Basin using Sentinel-2 satellite remote sensing images at a 10 m resolution. Various vegetation indices, including the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Water Index (NDWI), the Enhanced Vegetation Index (EVI), the Modified Soil Adjusted Vegetation Index (MSAVI), and others, were computed and analyzed for different crops. The Google Earth Engine (GEE) platform was utilized for validation point sampling based on plot objects. The random forest (RF) classification method was successfully employed to classify and identify major crops in the study area (wheat, maize, rice, and soybean), as well as wetlands, tree cover, grassland, water, and constructed land, with an overall classification accuracy of 86%. Tree cover dominated the land cover, constituting 62%, while wheat, maize, rice, and soybeans accounted for 7% of the total area. Of these, soybeans occupied the largest area (57,646.60 hectares), followed by rice (53,209.53 hectares), maize (39,998.37 hectares), and wheat (8782.31 hectares). This study demonstrated that sample selection based on plot objects facilitates efficient sample labeling, providing insights into crop classification in other, potentially larger, areas. This method simultaneously distinguishes wetland, cultivated land, and forest features, supporting further integrated investigations for more natural resources.

Influence of wildfires on the conflict (2006–2022) in eastern Ukraine using remote sensing techniques (MODIS and Sentinel-2 images)

Humans primarily alter fire regimes in three ways: changing the distribution and density of ignitions, shifting the seasonality of burning, or altering available fuels. Remote sensing techniques are a highly feasible and effective tool for monitoring wildfires, delineating burn perimeters, describing patterns of fire occurrence, characterizing the degree of intensity, or fire risk mapping. In this study MODIS and Sentinel-2 satellite images were used to analyze the burned areas in the Luhansk and Donetsk Regions of Ukraine (2006–2022), and specifically how the political and military hostilities in 2014 impacted fire activity. The burned areas detected by the MODIS sensor were selected using a size (32 ha) and temporal (May–October) filter. The Relativized Burn Ratio (RBR) index was used to identify burned areas, while the Normalized Difference Water Index (NDWI) was employed to discriminate the water bodies in Sentinel-2 images. We analyzed the burned area data over the 17-year period, eight of them during the years before the conflict and the other nine after the conflict. The results show that burned area has increased from the pre-conflict period (2008–2013) by 689,898 ha to the post-conflict period (2014–2022) around the contact line. In addition, the frequency of fires has increased in the post-war period, especially towards the south of Donetsk. Finally, Sentinel-2 imagery was used to map Large Wildfires (LAWF), which means fires with a burned area >500 ha. Our study concluded that MODIS MCD64A1 underestimates the burned area with a recorded difference of up to 40% of the area in one of the analyzed fires. In addition, an analysis of the burned areas classified according to the main land use has been performed for both sensors. The results obtained show a regression for the total area of R2 = 0,97 and both sensors show high correlation between them for each land use analyzed. Grasslands and agricultural land covers were most affected by LAWFs during the conflict period (2014–2022), noting that fires near urban entities were also more frequent in the war period.

Spatial distribution of Japanese encephalitis cases and correlated geo-environmental factors in southern and northern counties of China

Japanese encephalitis (JE) is a mosquito-borne disease with a spatial distribution that is linked to geo-environmental factors. The spatial distribution of JE cases and correlated geo-environmental factors were investigated in two critical counties in southern and northern China. Based on maps, enhanced thematic mapper (ETM) remote sensing datasets from Landsat and spatial datasets of JE cases, spatial distribution and spatial cluster analyses of JE cases at the village scale were performed by using the standard deviational ellipse and Ripleys K-function. Global and regional spatial cluster analyses of JE cases were also performed by using Moran's index. Regression analysis was used to analyze the relationships between geo-environmental characteristics and the risk of JE cases. At the study sites, the JE cases were not spatially clustered at the village or district (global) level, whereas there was a spatial cluster at the district (local) level. Diversity-related features for JE patients at the district and village levels were detected at two sites. In the southern counties, the distance of a village from a road was related to the village-level JE risk (OR: 0.530, 95 CI: 0.297–0.947, P = 0.032), and the number of township-level JE cases was linked to the distance of the district center from the road (R =-0.467, P = 0.025) and road length (R = 0.516, P = 0.012) in the administrative area. In northern China, the modified normalized difference water index (MNDWI) in the 5 km buffer around the village was related to village-level JE risk (OR: 0.702, 95% CI: 0.524–0.940, P = 0.018), and the number of township-level JE cases was related to the MNDWI in the administrative region (R =-0.522, P = 0.038). This study elucidates the spatial distribution patterns of JE cases and risk, as well as correlated geo-environmental features, at various spatial scales. This study will significantly assist the JE control efforts of the local Centers for Disease Control and Prevention (CDC), which is the base-level CDC, particularly concerning the allocation of medicine and medical staff, the development of immunological plans, and the allocation of pesticides and other control measures for the mosquito vectors of JE.

Fusion of diverse data sources for flood extent mapping and risk assessment in Sindh: A comparative study of inundation mapping approaches

Accurate and timely mapping of flood extent is crucial for effective disaster response and management. However, existing methods often face challenges in integrating diverse data sources and leveraging advanced techniques for precise inundation mapping. This research aimed to address this gap by proposing and evaluating four different methods for improving flood extent mapping in Sindh, Pakistan, through the integration of various data sources, including precipitation, Synthetic Aperture Radar (SAR), buildings, population, and optical data. The first strategy involves categorizing post-flood SAR data immediately using non-parametric (Support Vector Machine (SVM) and Random Forest (RF)), parametric (Maximum Likelihood Estimation (MLE)), and cluster (ISO) algorithms for Vertical transmit and Vertical received (VV) and Vertical transmit and Horizontal received (VH) polarisations. The second method relies on Otsu's method for automatic thresholding on post-flood event VH and VV data. The third approach utilizes both pre- and post-flood event data by creating a stack of Pre-event VH, Pre-event VV, Post-event VH, and Post-event VV data, which is then classified using the SVM, RF, MLE and ISO algorithms. Lastly, the fourth method employs the stack of Principal Component Analysis (PCA) bands, consisting of PCA components 1, 2, and 3, to categorize data for flooding using the SVM, RF, MLE and ISO algorithms. The rationale behind evaluating these diverse methods was to identify the most effective approach for accurately mapping flood extent by exploiting the strengths of different algorithms and data processing techniques. The accuracy and precision of these approaches were rigorously evaluated using Landsat-9 Normalized Difference Water Index (NDWI) as a reference dataset, along with error matrix and compound value (Cv) metrics. The findings show that a PCA-based technique with SVM is marginal superior (based on the overall accuracy and Kappa coefficient with value of 92.20 % and kappa coefficient 0.821) than the other approaches tested in the study. By reducing the dimensionality of the dataset while preserving relevant information, PCA offers a computationally efficient approach to flood extent mapping. Among all the approaches, the collective performance of the algorithms was computed using the Cv metric. The SVM algorithm with a Cv of 1.25 demonstrated the best performance, followed by the RF algorithm with Cv of 1.5, and the MLE algorithm with Cv of 2.5. The worst performing algorithm among all is found to be ISO with Cv of 4. It comes into view that first and second approach underperformed in VH polarisation compared to the VV polarisation and provide a more accurate representation of flooded area in VV polarisation. The flood extent with highest accuracy, together with the world's gridded population, latest Microsoft's Global ML Building Footprints, and OpenStreetMap building data are used in conjunction to estimate the number of people and buildings at risk within the study area.

Revealing the Urban Heat Island: Investigating Spatiotemporal Surface Temperature Dynamics, Modeling, and Interactions with Controllable and Non-controllable Factors

The unchecked growth of urban populations has spurred rapid and unsustainable urban expansion, exacerbating environmental degradation both locally and globally. This research offers a comprehensive analysis of the Urban Heat Island (UHI) phenomenon in Bangalore and Hyderabad, India, focusing on its spatial and temporal distribution and its correlation with air pollutants. Conducted over the course of summer and winter seasons from 2001 to 2021, this study advances our understanding of UHI dynamics in rapidly urbanizing regions, shedding light on the complex interplay between urbanization, climate, and air quality. The primary objective of this study is to assess and compare the predictive performance of baseline Random Forest (RF) and XGBoost models for forecasting Land Surface Temperature (LST) in Hyderabad, India, while examining the impact of integrating spatial information into these models. Incorporating an extensive array of input variables, ranging from spectral indices to terrain features, land use/land cover (LULC) data, atmospheric parameters, and spatial data, the analysis reveals significant relationships between LST and various factors. Notably, Aerosol Optical Depth (AOD) and Normalized Difference Built-up Index (NDBI) exhibit strong positive correlations with LST, while Enhanced Vegetation Index (EVI) demonstrates a pronounced negative correlation. Similarly, Modified Normalized Difference Water Index (MNDWI) displays the strongest positive correlation with LST (r = 0.49), whereas EVI exhibits the most notable negative correlation (r = -0.67). Additionally, Modified Built-up Index (MBI) demonstrates a noteworthy negative correlation (r = -0.48) with LST. Atmospheric components such as CO, NO2, and O3 also show significant positive correlations with LST, with NO2 displaying the highest correlation during winter in urban areas. XGBoost emerges as the superior model, achieving high R2 values of 0.84 for Bangalore and 0.88 for Hyderabad. Integration of spatial information consistently enhances the performance of both RF and XGBoost models, resulting in reduced Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) values. However, predictions over water bodies tend to be underestimated. The study underscores the importance of incorporating spatial data for accurate LST prediction, with implications for environmental monitoring and management. Despite the advancements made, further research is needed to elucidate the specific mechanisms through which spatial information improves model performance, suggesting avenues for future investigation and refinement of modeling methodologies. At the intersection of rapid urban growth and environmental conservation, these findings are poised to inform policymakers, urban planners, and researchers, guiding them towards innovative and sustainable urban development strategies aimed at mitigating the adverse impacts of rapid urban expansion while promoting ecosystem well-being. The study contributes to spatial modeling strategies and refines LST prediction methodologies, offering valuable insights for addressing the challenges posed by urbanization and climate change in rapidly urbanizing regions.

Monitoring of land-use change in 2 periods by using Remote Sensing Technology and Spectral indices

This research aims to monitor land-use change in 2 periods, i.e., 2019 and 2023, by using Remote Sensing Technology and band indices. Research methodology consisted of: 1) classifying land-use based on data obtained from Sentinel-2 satellite into 4 types including: agriculture, forest, urban, and water; 2) analyzing Normalized Difference Vegetation Index (NDVI), Normalized Difference Built - up Index (NDBI), and Normalized Difference Water Index (NDWI) as well as analyzing relationship between land-use and NDVI, NDBI, and NDWI; and 3) comparing land-use change of 2019 and 2023. The results revealed that land-use change in Mueang Maha Sarakham District, Maha Sarakham Province, in 2 periods of time was as follows: reduced areas were agriculture and forest, and increased areas were urban and water.

Precision Estimation of Crop Coefficient for Maize Cultivation Using High-Resolution Satellite Imagery to Enhance Evapotranspiration Assessment in Agriculture.

The estimation of crop evapotranspiration (ETc) is crucial for irrigation water management, especially in arid regions. This can be particularly relevant in the Po Valley (Italy), where arable lands suffer from drought damages on an annual basis, causing drastic crop yield losses. This study presents a novel approach for vegetation-based estimation of crop evapotranspiration (ETc) for maize. Three years of high-resolution multispectral satellite (Sentinel-2)-based Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Red Edge Index (NDRE), and Leaf Area Index (LAI) time series data were used to derive crop coefficients of maize in nine plots at the Acqua Campus experimental farm of Irrigation Consortium for the Emilia Romagna Canal (CER), Italy. Since certain vegetation indices (VIs) (such as NDVI) have an exponential nature compared to the other indices, both linear and power regression models were evaluated to estimate the crop coefficient (Kc). In the context of linear regression, the correlations between Food and Agriculture Organization (FAO)-based Kc and NDWI, NDRE, NDVI, and LAI-based Kc were 0.833, 0.870, 0.886, and 0.771, respectively. Strong correlation values in the case of power regression (NDWI: 0.876, NDRE: 0.872, NDVI: 0.888, LAI: 0.746) indicated an alternative approach to provide crop coefficients for the vegetation period. The VI-based ETc values were calculated using reference evapotranspiration (ET0) and VI-based Kc. The weather station data of CER were used to calculate ET0 based on Penman-Monteith estimation. Out of the Vis, NDWI and NDVI-based ETc performed the best both in the cases of linear (NDWI RMSE: 0.43 ± 0.12; NDVI RMSE: 0.43 ± 0.095) and power (NDWI RMSE: 0.44 ± 0.116; NDVI RMSE: 0.44 ± 0.103) approaches. The findings affirm the efficacy of the developed methodology in accurately assessing the evapotranspiration rate. Consequently, it offers a more refined temporal estimation of water requirements for maize cultivation in the region.

Fluvio-geomorphic change of the Padma-Meghna river course using the NDWI and MNDWI techniques

ABSTRACT This study intends to investigate the fluvial-geomorphic change and erosion-accretion processes of two significant rivers. The 2149 km2 research area’s overall erosion and accretion were depicted using multi-temporal Landsat images and Normalized Difference Water Index, Modified Normalized Difference Water Index methods to measure and investigate change. However, focus has been placed on four unique sections that are prone to erosion, and the bank migration of the section has been measured in order to depict the actual state of erosion. The Padma-Meghna River eroded approximately 11.0, 77.0, and 13.0 km2 between 1973–1978, 1978–1989, and 1989–1994. However, 78.0 and 48.0 km2 of accretion occurred in the years between 1994 and 1999 and 1999 and 2004. Moreover, between 2004 and 2021, the river’s erosional activity was remarkably dynamic compared to deposition. Besides, 64 km2 and 4 km2 of land accretion occurred during 2008–2014 and 2014–2019, while 77 km2 and 68 km2 of land eroded during 2004–2008 and 2019–2021. The overall rates of erosion and accretion are 5.12% and 4.04%, respectively. Statistically, both banks of the Padma River eroded about 2 km from 1973 to 2021. Padma-Meghna confluences are more unstable and susceptible to change. The Meghna’s left bank moved 4.5 km east between Goshairhat and Haimchar in 1989, and in 2008, the river shifted 4.8 km west from the river of 1994. In 1989, it moved 5 km east from a previous location between Goshairhat and Haiderganj, and in 2008, it moved 6.3 km west from the 1989 river. The bank migrated 12 km eastward in 2014 from where it was in 2008 through the bar development. The Mehendiganj Upazila’s eastern side lost more than 7.4 km between 1973 and 2021, and the current major channel is simply 7.4 km west of the 1973 river. The findings of the dynamic changes might be useful to planners, developers, researchers, and government agencies as they develop and put into practice new development initiatives and river basin management.

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

SPATIOTEMPORAL ANALYSIS OF DENGUE CASES IN CEBU CITY FROM YEAR 2015 TO 2022

Abstract. Long-term climate changes, including increased temperature, shift in precipitation, wind patterns, and other climate factors, can disrupt the balance of nature and have significant implications in the transmission of dengue fever. This study investigated the spatial and temporal dynamics of dengue cases in Cebu City, a key metropolitan area in the Philippines characterized by a significant rate of urbanization in recent years. Climate Engine (CE), a cloud-based computing and visualization tool, was utilized in this study for database sources of Landsat 8 pre-processed satellite images. Time-series dataset of land surface temperatures (LST) and varying environmental indices such as Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Normalized Difference Built-up Index (NDBI) were examined to investigate the effects of increased urban surface temperatures, expanding urban structures, and diminishing vegetation in Cebu City on dengue cases from 2015 to 2022. The spatial distribution of dengue cases was analyzed through GeoDA to identify hotspots within the city. The annual dengue cases in Cebu City exhibit a temporal trend with a peak in 2016 (4637 cases) and a lowest point in 2021 (399 cases), the year when the pandemic struck. Most dengue cases were recorded between June and December, exhibiting a strong seasonal pattern, and primarily concentrated within the wet season. Barangay Guadalupe topped the number of cases (1781) followed by Barangay Lahug (1219 cases), and Barangay Labangon (1128 cases) from 2015 to 2022. These three residential barangays are in proximity to each other, indicating a potential localized clustering of dengue cases in neighboring areas. The equation derived from the linear regression model serves as a predictive tool for estimating dengue cases in Cebu City and is expressed as Dengue cases = −28.436 + 2.137 (LST) − 13.943 (NDVI) + 8.565 (NDWI) − 10.217 (NDBI). The findings of this study will have practical implications for urban planning and the development of local policies aimed at mitigating the rise in dengue cases.

Evaluation of the change in crop structure under the impact of flood prevention dikes in the Long Xuyen Quadrangle in the period 2005-2020

The study was conducted to monitor spatial fluctuations of dikes affecting the rice crop structure and evaluate changes in rice crop structure inside and outside the dikes in the Long Xuyen Quadrangle in the period 2005-2020. That serves as a basis for assessing the current status of flood control and the changing trend of rice production in the region in the future. The study uses LANDSAT images and applies the normalized difference water index (NDWI) and shoreline extraction to determine the current condition of dikes in the Long Xuyen Quadrangle region, while combining a series of normalized difference vegetation index (NDVI) on moderate resolution imaging spectroradiometer (MODIS) image data to determine the structure of rice crops in 2005 and 2020. The results show that the embankment length increased by 1,910.15 km compared to 2005 and the natural embankment area increased to 123,020.31 ha (accounting for 24.63% of the area’s natural area) in the period 2005-2020. The farming system in the study area impacted by flood prevention dike changes from other crops to triple-rice crop cultivation. Specifically, the total area of the other cultivation types converted into triple-rice crop cultivation is 90,904.77 ha, accounting for 51.92% of the total area of structural transformation of farming types, concentrated in districts with large dike areas such as Thoai Son, Chau Phu, and Chau Thanh districts (An Giang province). The research results contribute to supporting environmental management and orienting rice production trends in the region in the future.

WBGT prediction with high spatial resolution using actual measurement data and data acquired using infrared sensors mounted on UAVs

Spatial understanding of heat stress indices is important to prevent the worsening of heat-related health problems. The objective of this study is to understand heat stress indices with high spatial and temporal resolution using actual measurement data and data acquired using infrared sensors mounted on unmanned aerial vehicles (UAVs). Two study sites were established to examine the relationship between green space and heat stress indices and the versatility of the developed method. The wet bulb globe temperature (WBGT), a heat stress index, was measured on extremely hot days. Thermal infrared, the normalized difference vegetation index, and the normalized difference water index were obtained from UAV images. Extreme gradient boosting, a supervised machine learning method for gradient boosting decision trees, was used to predict the WBGT for the study area using data from actual measurement points as training data. The maximum and average root mean square errors of the predicted WBGT were 2.8 and 1.4, respectively. The second measurement session, which was closest to noon, showed a greater WBGT reduction effect because of vegetation and moisture. This study successfully predicted the WBGT with high spatial and temporal resolution. This achievement is expected to contribute to the prevention of heat stress.

Enhancing Flood Impact Analysis through the Integration of Landsat and MODIS Imagery

This article explores the efficacy of integrating Landsat and MODIS satellite imagery for comprehensive flood impact analysis. By employing advanced remote sensing technologies and sophisticated data processing techniques, this study offers a methodological framework that enhances the precision and depth of environmental analysis. The core methodology involves the systematic processing of satellite data, including radiometric and geometric corrections, combined with the use of analytical indices such as the Normalized Difference Water Index (NDWI) and the Enhanced Vegetation Index (EVI). These indices play a crucial role in accurately delineating water bodies and assessing the extent of flooding. The approach not only improves the reliability of flood mapping but also contributes to the broader understanding of environmental changes and aids in effective disaster management. Through this study, we demonstrate how strategic data integration can provide valuable insights for policymakers, enhancing responses to environmental crises.

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.

Relationship between monthly surface water derived from Sentinel-2 imagery and meteorological data (precipitation and evaporation) at Baghdad, Iraq

Abstract This study was carried out using two main monthly datasets: satellite images and some meteorological parameters (like precipitation and evaporation) for 2 years (2018 and 2021). Using the fine spatial resolution images acquired from Sentinel-2, digital maps of surface water areas were produced by calculating the Modified Normalized Difference Water Index for all months in QGIS and then classifying them into two main categories (water and non-water bodies). The results show that the largest areas were in January 2021 (77.2 km2) and 40.7 km2 in November 2018, while the lowest (18.7 km2) were in June 2021 and July 2018. Monthly mean values of cumulative precipitation and evaporation were used to find their relationships with surface water areas. The results show that the year 2021 was a severe dry year, whereas the lowest precipitation (25 mm) in that year was associated with higher evaporation losses (3,021.8 mm) during this year. When combined with surface water areas and meteorological parameters, a non-linear relationship between water areas and evaporation was found for both years, and a linear relationship between precipitation and water areas for the wet year of 2018.

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.

Development of a spatial framework for flash flood damage assessment and mitigation by coupling analytics of machine learning and household level survey data – A case study of rapid collaborative assessments and disbursement of public funds to the affectees of floods 2022, Punjab Pakistan

In August 2022, one of the most devastating floods in the history of Pakistan was triggered due to heavy and exceptionally high monsoon rainfall. It has affected thirty-six million people across the country and agriculture losses were recorded in the most productive Indus plains which aggravated the food insecurity in the country. This study focuses on the two districts of Punjab provinces includes DG Khan and Rajanpur, where huge flood damages were reported. As part of damage and loss assessment methodologies, GIS/RS based approach is developed in this study to estimate flood extent in DG Khan and Rajanpur. Field survey was conducted using android based devices with the help of military and government organizations such as PDMA, to verify and record flood damages. Information regarding damage includes property, livestock, people, crop and other damages with real-time images captured and stored via android app and flood data monitoring was visualized on a web-based dashboard. SAR data (Sentinel 1-A) was used to calculate and verify extent of the flood along with Sentinel 2-A dataset to extract Land cover classes. SAR flood maps were validated using the survey data retrieved during flood events. This data was compared with the Sentinel-1A dual polarised (VV & VH). The SAR data (Sentinel-1A) was also collected for the same day to extract the Flood Extent of August/September 2022 using Modified Normalized Difference Water Index (mNDWI). Hence, flood inundation map was prepared using polarisation. This examination is confirmed through the kappa coefficient (κ), which is 0.88 for VV polarisation and 0.86 for VH polarisation. In VV polarisation, PA and UA of ‘water’ are less than of all ‘other classes. Mauza level mapping was also conducted to identify the damaged mauzas in both floods affected districts to figure out damaged villages and find out damaged property with khasra or mauza name. GIS RS based and survey-based model was developed in this research, which replicated on two districts of Punjab to assess flood damage, it will help Pakistan government to always asses flood damages to crop, houses, livestock and other assets. This helped to communicate with the local governments and identify and rank high affected villages; so that priority shall be given as per intensity of the damages. An android application for flood survey was developed. The flood survey application provided facility to estimate the on-ground reality of each individual, property, crop and livestock damage due to flood. This application was mainly used for the assessment purpose that provides detail information with precise location and real visualization of the damage assets. Joint survey teams were constituted including members from the Provincial Disaster Management Authority, National Disaster Management Authority, Pakistan Army, Local Government and the Urban Unit. Each village classified as flood damages/affected was visited by the survey team through a proper survey plan. After all the flood data was recorded, now the next step was to build transparent system to distribute funds to the flood victims. For this purpose, Bank has developed dashboard to distribute funds to the affected by the Government of Punjab.

LAND USE LAND COVER CHANGE MAPPING FROM SENTINEL 1B < 2A IMAGERY USING RANDOM FOREST ALGORITHM IN CÔTE D’IVOIRE

Monitoring crop condition, soil properties, and mapping tillage activities can be used to assess land use, forecast crops, monitor seasonal changes, and contribute to the implementation of sustainable development policy. Agricultural maps can provide independent and objective estimates of the extent of crops in a given area or growing season, which can be used to support efforts to ensure food security in vulnerable areas. Satellite data can help detect and classify different types of soil. The evolution of satellite remote sensing technologies has transformed techniques for monitoring the Earth’s surface over the last several decades. The European Space Agency (ESA) and the European Union (EU) created the Copernicus program, which resulted in the European satellites Sentinel-1B (S1B) and Sentinel-2A (S2A), which allow the collection of multi-temporal, spatial, and highly repeatable data, providing an excellent opportunity for the study of land use, land cover, and change. The goal of this study is to map the land cover of Côte d’Ivoire’s West Central Soubre area (5°47′1′′ North, 6°35′38′′ West) between 2014 and 2020. The method is based on a combination of S1B and S2A imagery data, as well as three types of predictors: the biophysical indices Normalized Difference Vegetation Index “(NDVI)”, Modified Normalized Difference Water Index “(MNDWI)”, Normalized Difference Urbanization Index “(NDBI)”, and Normalized Difference Water Index “(NDWI)”, as well as spectral bands (B1, B11, B2, B3, B4, B6, B7, B8) and polarization coefficients VV. For the period 2014–2020, six land classifications have been established: Thick_Forest, Clear_Drill, Urban, Water, Palm_Oil, Bareland, and Cacao_Land. The Random Forest (RF) algorithm with 60 numberOfTrees was the primary categorization approach used in the Google Earth Engine (GEE) platform. The results show that the RF classification performed well, with outOfBagErrorEstimates of 0.0314 and 0.0498 for 2014 and 2020, respectively. The classification accuracy values for the kappa coefficients were above 95%: 96.42% in 2014 and 95.28% in 2020, with an overall accuracy of 96.97% in 2014 and 96 % in 2020. Furthermore, the User Accuracy (UA) and Producer Accuracy (PA) values for the classes were frequently above 80%, with the exception of the Bareland class in 2020, which achieved 79.20%. The backscatter coefficients of the S1B polarization variables had higher GINI significance in 2014: VH (70.80) compared to VH (50.37) in 2020; and VV (57.11) in 2014 compared to VV (46.17) in 2020. Polarization coefficients had higher values than the other spectral and biophysical variables of the three predictor variables. During the study period, the Thick_Forest (35.90% ± 1.17), Palm_Oil (57.59% ± 1.48), and Water (5.90% ± 0.47) classes experienced a regression in area, while the Clear_Drill (16.96% ± 0.80), Urban (2.32% ± 0.29), Bareland (83.54% ± 1.79), and Cacao_Land (35.14% ± 1.16) classes experienced an increase. The approach used is regarded as excellent based on the results obtained.