Normalized Difference Water Index Research Articles

Spatiotemporal analysis of shoreline change trends and adaptation in Bali Province, Indonesia

Shoreline change is a dynamic and complex phenomenon influenced by various natural and anthropogenic factors. Monitoring these changes is crucial for understanding coastal dynamics, managing resources, and mitigating the impacts of coastal erosion and accretion. Remote sensing plays a vital role in monitoring shoreline change. This technology has emerged as a powerful tool for accessing and monitoring shoreline changes due to its ability to capture large-scale spatial data over different periods. This study investigated the dynamic changes in shoreline position in Bali Province from 2016 to 2021 using high-resolution PlanetScope imagery data. Multitemporal shorelines were extracted using the Normalized Difference Water Index (NDWI) to delineate seawater from distinct land-cover types. The end point rate (EPR) model was then used to statistically quantify the shoreline change rates. The results revealed variations in the positions of the shorelines throughout the study, with rapid erosion emerging as a major concern in Bali Province, primarily driven by human activities and/or coastal hydrodynamics. During the study period, the shoreline in Bali Province decreased from 668.64 km to 662.59 km at an average rate of -1.21 m/yr due to continuous erosion, with the most significant retreat occurring at Klungkung Regency. On the other hand, human-caused shoreline advancement, such as land reclamation, harbor, and airport construction, has resulted in a net land increase of 1.25 km2. The protection strategies include structural interventions such as breakwaters and seawalls. The finding provides valuable information that will be helpful for coastal planning and management to reduce the loss of the economy and safeguard the future coastal environment in Bali’s coastal areas.

Vegetation discrimination based on chlorophyll prediction in Marshy wetland using Unmanned Aerial Vehicles

Abstract Wetlands are an integral part of our global ecosystems and play crucial roles in ecological functions such as carbon sequestration, flood mitigation, water purification, and recreational activities. The Ramsar Convention is the most significant wetland protection pact and is doing tremendous work in conserving wetlands worldwide. However, the wetlands area is still under threat due to anthropogenic activity. The current study utilized drone images, chlorophyll measurements and machine leaning to discriminate and map vegetation at marsh wetland area—the Ramsar site. The high‐resolution, multispectral imagery is acquired using a drone‐mounted MICAsense sensor. Eight spectral indices such as Normalized Difference Water Index (NDWI), Two‐Band Algorithm (2BDA), Normalized Difference Chlorophyll Index (NDCI), Normalized Difference Vegetation Index (NDVI) Enhanced Normalized Difference Vegetation Index (ENDVI), Green Normalized Difference Vegetation Index (GNDVI), and Normalised Difference RedEdge (NDRE) were calculated on the acquired imagery in order to discriminate the different vegetation covers such as floating aquatic vegetation (FAV), open water, and other vegetations types. These include the following: Eichhornia, Nymphea, Oleracea, Paspalam, and Oryza from agriculture land at the study site. Two models (viz., the Taylor plot and the Lek Profile methods) were employed to assess the sensitivity of the spectral indices for prediction of chlorophyll and vegetation discrimination. It is inferred from both methods that NDCI was most sensitive for chlorophyll prediction of vegetation followed by NGRDI/ ENDVI/ 2BDA and NDVI for chlorophyll prediction in wetland ecosystems. Further, three machine learning algorithms, support vector machine (SVM), random forest (RF), and gradient tree boost (GTB), were utilized for classification, and the performance accuracy of GTB was found to be the highest (0.893), followed by RF (0.851) and SVM (0.723). The GTB algorithm was applied over NDCI for vegetation discrimination. The study revealed that Eichhronia sp. is abundantly present at the study site; hence, strategic management plans should be carried out for the eradication of invasive species and proper management of wetland vegetation.

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.

Assessment of Agricultural Drought Using the Normalized Difference Drought Index (NDDI) to Prediction Drought at Corong River Basin

As a complex and widespread natural phenomenon, drought poses a significant threat to the agricultural sector, especially in developing countries, resulting in significant economic losses. Its close relationship with water resilience and crop production necessitates sophisticated monitoring approaches for agricultural drought. Leveraging satellite remote sensing technology and various data types such as multispectral, thermal infrared, and microwave, can monitors drought on a large scale. This technology provides a comprehensive perspective for timely and spatial data collection, facilitating monitoring vegetation in vast agricultural areas. The study focuses on developing an agricultural drought model from 2017 to 2021, using Landsat 8 imagery. The model integrates the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI), resulting in the establishment of the Normalized Difference Drought Index (NDDI) method. To predict agricultural drought in the Corong River Basin, the study employs the Seasonal Autoregressive Integrated Moving Average (SARIMA) model. Findings reveal varying degrees of dryness in the Corong River Basin, with 77% categorized as Strong dry conditions, 1% as Dry, 0.3% as Moderate wetness, and 21.6% as wetness. Drought predominantly occurs between July and October, impacting approximately 78% of the total dry area and extending across almost the entire region. The SARIMA (0,0,1)(3,0,0)12 model, with a MAPE value of 0.2399, emerges as the most effective for predicting agricultural drought. These forecasted results provide critical insights into the level of agricultural drought in the Corong River Basin and valuable information for drought mitigation strategies, especially in regulating the distribution of irrigation water.

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.

Monitoring the Invasion of S. alterniflora on the Yangtze River Delta, China, Using Time Series Landsat Images during 1990–2022

Spartina alterniflora (S. alterniflora) has grown rapidly in China since its introduction in 1979, showing the trend of alien species invasion, which has seriously affected the ecosystem balance of coastal wetlands. The temporal and spatial expansion law of S. alterniflora can be obtained through remote sensing monitoring, which can provide a reference and basis for S. alterniflora management. This paper presents a method for extracting and mapping S. alterniflora based on phenological characteristics. The coastal areas of the Yangtze River Delta Urban Agglomeration are selected as the research area, and the Landsat time series data from 1990 to 2022 on the Google Earth Engine (GEE) platform are used to support the experiment in this paper. Firstly, the possible growing area of S. alterniflora was extracted using the normalized differential moisture index (NDMI), normalized differential vegetation index (NDVI), and normalized differential water index (NDWI); Then, the time series curve characterizing the phenological characteristics of vegetation was constructed using the vegetation index to determine the difference phase of phenological characteristics between S. alterniflora and other vegetation. Finally, a decision tree was constructed based on the phenological feature difference phase data to extract S. alterniflora, and it is applied to the analysis of temporal and spatial changes of S. alterniflora in the study area from 1990 to 2022. The results show that the area of S. alterniflora increased from ~1426 ha in 1990 to ~44,508 ha in 2022. However, the area of S. alterniflora began to show negative growth in 2015 due to the construction of nature reserves and ecological management. The results of correlation analysis showed that the growth of C. japonicum was significantly affected by temperature stress and weakly affected by precipitation. This study verified that Landsat time series images can effectively extract vegetation phenological information, which has strong feasibility for extraction and dynamic monitoring of S. alterniflora and provides technical support for the management and monitoring of invasive plants in coastal wetlands.

A comprehensive investigation on the effect of UHI in Baripada city using geo‐spatial technique and MLTHP model

AbstractAccording to the Times of India, Baripada city recorded the highest temperature in the world on April 14, 2023, at 43.5°C. It is located in the south‐west direction of the Similipal Biosphere Reserve. Observing the third highest temperature in the world, we are interested in finding out the reasons affecting climate change. This paper investigates the reasons for the temperature change in Baripada city and its surroundings by considering factors like Normalized Difference Vegetation Index (NDVI), Normalized Multi‐band Drought Index (NMDI), Normalized Difference Water Index (NDWI), Normalized Difference Built‐up Index (NDBI), forest fires, rainfall patterns, wind patterns, and registration of motor vehicles. For this study, data has been collected from USGS (United States Geological Survey), NASA POWER, Forest Survey of India, ArcGIS Online, etc. The study of NDVI shows a significant relationship with the LST. Similarly, NMDI shows an abnormal change in the drought index from 0.092 in 1990 to 0.276 in 2022, and NDWI shows a continuous reduction of the water index of −0.382 in 1990 and −0.176 in 2010, followed by −0.212 in 2022. NDBI values show that the urban build‐up index is increased to an extent due to rapid urban settlements removing natural vegetation. The overall forest fire count found an abnormal rise in the Baripada region during 2011 and 2022 to 143 times. Focusing on all the above factors, an experimental study is finally carried out to confirm the rise in temperature using time‐series analysis and a multi‐layer locally tuned hidden layer perceptron (MLTHP) model to ensure the Times of India's report.

Improving the accuracy of mangrove forest mapping using sentinel image and developed classification algorithm based on machine learning

ABSTRACT The national mangrove rehabilitation required mangrove spatial information with very high accuracy. This study successfully developed the classification algorithm measurable for mapping mangrove forest change based on machine learning. We also modified the recursive feature elimination (RFE) method to obtain the most optimal feature importance (FI). The results showed that the developed classification algorithm was built based on the five most important features including digital elevation model (DEM), near-infrared (NIR), normalized difference moisture index (NDMI), normalized difference water index (NDWI), and distances from brackish water river (DBWR). The developed algorithm increased overall and kappa accuracy by 0.48% and 0.01, respectively.

Examining the Effects of a Flood Event in the Lower Ceyhan Basin in 1980 Using Historical Satellite Data

Analysis of past flood events contributes forecasting of effects of future flood events. Flood maps have been created in order to assess flood hazards in planning projects and to identify flood-inundated regions with flood damage following flood occurrences. Flood mapping in the context of flood monitoring enables development of flood management strategies to protect life and property. Although conventional terrestrial observations and measurements in flood control have been constrained by topographical and meteorological circumstances, remote sensing provides decision support with quick analysis capability. The flood event that occurred in the Lower Ceyhan Basin of Turkey in 1980 was examined in this work utilizing satellite-based remote sensing techniques, and flood inundation areas were calculated using NDWI (Normalized Difference Water Index). As a result, it was determined that 3493.45 ha in the north of Karataş in the Lower Ceyhan Plain, 7799.42 ha between Bahçe, Akdeniz, and Yumurtalık, 7404.9 ha around Çatalpınar and Yakapınar in the Lower Ceyhan Plain, and approximately 24890 ha in the Upper Ceyhan were affected by the flood event in 1980.