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1
Sarkar, Bapi, Sribas Patra, and Mallikarjun Mishra. "Geospatial Analysis of the Relationship Between Land Surface Temperature and Land Use/Land Cover Indices: A Study of Raiganj Municipality, West Bengal, India." Nature Environment and Pollution Technology 24, no. 2 (2025): B4245. https://doi.org/10.46488/nept.2025.v24i02.b4245.
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The present study is focused on the estimation of Land Surface Temperature (LST) and its relationship with three Land Use and Land Cover (LULC) indices--Normalised Difference Vegetation Index (NDVI), Normalised Difference Water Index (NDWI), and Normalised Difference Built-up Index (NDBI) in Raiganj Municipality, India. Landsat-5 TM (2001 & 2011) and Landsat-8 OLI (2021) satellite images were used, processed, and analyzed in the ArcGIS. The study observed that the values of LST and NDBI were increased by +0.9˚C and +0.71, and the values of NDVI and NDWI were decreased by -0.20 and -0.34 during 2001- 2021. The highest LST is observed over the built-up spaces and the lowest over vegetation cover and water bodies. The result indicates LST has a significant positive correlation with NDBI and a negative correlation with NDVI and NDWI. LST is increased due to dramatic changes in LULC especially in unplanned infrastructural development and losses in green and blue spaces.
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Afrin, Shaikh* Dhruva Jani Hitesh Solanki. "Assessment of Vegetation Condition in Lower Narmada Basin Using Remote Sensing and GIS." International Journal of Scientific Research and Technology 2, no. 3 (2025): 649–55. https://doi.org/10.5281/zenodo.15109793.
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The study employs remote sensing and GIS to evaluate vegetation conditions in the Lower Narmada Basin. This study calculates numerous spectral indices using Landsat data from 2015, 2020 and 2025, including the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Moisture Index (NDMI), and Normalized Difference Built-up Index (NDBI). The research revealed patterns in vegetation cover, water content, moisture levels, and built-up areas throughout the basin. The study shows that there is a substantial relationship between NDVI and NDWI, implying that vegetation expansion and water body loss occur concurrently. Furthermore, increased urbanization is associated with lower NDVI and NDMI, emphasizing the importance of balanced land management measures for ensuring ecological sustainability.
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Dr., Sukamal Maity. "Comprehensive analysis of Land Surface Temperature with different indices using Landsat – 8 (OLI TIRS) data in Kanpur Metropolis, India." Akshar Wangmay 1, Special Issue (2020): 144–51. https://doi.org/10.5281/zenodo.5234590.
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Urbanization has a significant impact on land use by interchanging areas of vegetation with the residential and commercial landscape which reflects a heterogeneous urban infrastructure; this escalates the land Surface Temperature (LST), which has become major Eco-environmental anxiety. Hence, the study of Land Surface Temperature (LST) and its correlation with other indices in urban areas using satellite data is more important. The present study focuses on the relation of LST with the selected indices based on Landsat 8 OLI (Operational Land Imager) and TIRS (Thermal Infrared Sensor) data in Kanpur Metropolis, India. These satellites data of October 20 were computed and analyzed through regression analysis between LST and Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), Normalized Difference Water Index (NDWI), and Normalized Difference Soil Index (NDSI). LST was retrieved by thermal data examination which represents the spatiotemporal distribution of surface temperature. Mean LST varied from 33.94 OC to 24.12OC. NDBI is reciting the built-up index and NDVI the proportion of vegetation in the Metropolis area. Correlation results of LST with NDBI, NDSI, and NDBI with NDSI, NDWI have shown a strong positive relationship i.e. 0.74, 0.72, 0.89, and 0.64 respectively along with the R2 = 0.545, 0.525, 0.787, and 0.407 respectively, whereas the correlation of, NDVI with NDBI and NDWI has a strong negative relationship i.e. – 0.76 and – 0.98 respectively along with the. R2 = 0.575 and 0.9615. A Global Moran’s I and LISA Index analysis was done for all of the indicators.
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Bala, R., R. Prasad, V. P. Yadav, and J. Sharma. "A COMPARATIVE STUDY OF LAND SURFACE TEMPERATURE WITH DIFFERENT INDICES ON HETEROGENEOUS LAND COVER USING LANDSAT 8 DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-5 (November 19, 2018): 389–94. http://dx.doi.org/10.5194/isprs-archives-xlii-5-389-2018.
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<p><strong>Abstract.</strong> The temperature rise in urban areas has become a major environmental concern. Hence, the study of Land surface temperature (LST) in urban areas is important to understand the behaviour of different land covers on temperature. Relation of LST with different indices is required to study LST in urban areas using satellite data. The present study focuses on the relation of LST with the selected indices based on different land cover using Landsat 8 OLI (Operational Land Imager) and TIRS (Thermal Infrared Sensor) data in Varanasi, India. A regression analysis was done between LST and Normalized Difference Vegetation index (NDVI), Normalized Difference Soil Index (NDSI), Normalized Difference Built-up Index (NDBI) and Normalized Difference Water Index (NDWI). The non-linear relations of LST with NDVI and NDWI were observed, whereas NDBI and NDSI were found to show positive linear relation with LST. The correlation of LST with NDSI was found better than NDBI. Further analysis was done by choosing 25 pure pixels from each land cover of water, vegetation, bare soil and urban areas to determine the behaviour of indices on LST for each land cover. The investigation shows that NDSI and NDBI can be effectively used for study of LST in urban areas. However, NDBI can explain urban LST in the better way for the regions without water body.</p>
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Gogoi, Tulumoni, and M. S. Rawat. "Study on NDVI and NDWI change detection of Gai river basin, NEI using GIS and remote sensing." Ecology, Environment and Conservation 30, Suppl (2024): S179—S183. http://dx.doi.org/10.53550/eec.2024.v30i06s.026.
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This paper is a study on the change detection of the Gai River basin. Researchers considered Normalised difference vegetation index and Normalised difference water index techniques for the detection of changes in vegetation and water bodies. Landsat 7 and 8/9 satellite imagery are utilised in ArcGIS, a raster calculator tool, to evaluate the ratio difference. The vegetation index ratios are -0.23 (low), 0.55 (high), -0.12 (low), and 0.52 (high) between 2003 and 2024. Moreover, the NDWI ratio value varies from -0.44 low to 0.28 (high) in 2003 and -0.46 (low) and 0.16 (high) in 2024. The difference in ratio values indicates the change detection areas. The prepared map of the NDVI and NDWI has shown the changes. Vegetation and water are crucial natural resources, but the changes in these resources impact our environment. So, maintenance and sustainable utilisation are needed to conserve those resources.
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Gogoi, Tulumoni, and M. S. Rawat. "Study on NDVI and NDWI change detection of Gai river basin, NEI using GIS and remote sensing." ECOLOGY, ENVIRONMENT AND CONSERVATION 30, Suppl (2024): S431—S435. http://dx.doi.org/10.53550/eec.2024.v30i05s.067.
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This paper is a study on the change detection of the Gai River basin. Researchers considered Normalised difference vegetation index and Normalised difference water index techniques for the detection of changes in vegetation and water bodies. Landsat 7 (13-05-2003) and 9 (11-12-2023) satellite imagery are utilised in ArcGIS, a raster calculator tool, to evaluate the ratio difference. The vegetation index ratios are -0.20 (low), 0.55 (high), -0.10 (low), and 0.59 (high) between 2003 and 2023. Moreover, the NDWI ratio value varies from -0.45 low to 0.26 (high) in 2003 and -0.53 (low) and 0.15 (high) in 2023. The difference in ratio values indicates the change detection areas. The prepared map of the NDVI and NDWI has shown the changes. Vegetation and water are crucial natural resources, but the changes in these resources impact our environment. So, maintenance and sustainable utilisation are needed to conserve those resources.
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Das, Susanta, Samanpreet Kaur, and Antarpreet Jutla. "Earth Observations Based Assessment of Impact of COVID-19 Lockdown on Surface Water Quality of Buddha Nala, Punjab, India." Water 13, no. 10 (2021): 1363. http://dx.doi.org/10.3390/w13101363.
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The novel coronavirus disease (COVID-19) halted almost all the industrial scale anthropogenic activities across the globe, resulting in improvements in water and air quality of megacities. Here, using Sentinel-2A data, we quantified impact of COVID-19 lockdown on the water quality parameters in one of the largest perennial creeks i.e., the Buddha Nala located in District Ludhiana in India. This creek has long been considered as a dumping ground for industrial wastes and has resulted in surface and ground water pollution in the entire lower Indus Basin. Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Chlorophyll Index (NDCI), Nitrogen Content Index (NI), Normalized Difference Turbidity Index (NDTI), and Total Suspended Matter (TSM) were compared prior (2019) and during (2020) lockdown in the creek. There was a significant enhancement in NDVI, NDWI, NDCI, and NI values, and reduction in NDTI and TSM values during the lockdown period. When compared with prior year (2019), the values of indices suggested an improvement in water quality and an indicative change in aquatic ecology in the creek. The impact of the COVID-19 lockdown on the improvement in water quality of Buddha Nala was more evident in the upstream and downstream sections than the middle section. This is intriguing since the middle section of the creek was continually impacted by domestic household effluents. The earth observation inspired methodology employed and findings are testament to the discriminatory power to employ remote sensing data and to develop protocols to monitor water quality in regions where routine surveillance of water remains cost prohibitive.
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Anak Kemarau, Ricky, and Oliver Valentine Eboy. "Application of Remote Sensing on El Niño Extreme Effect in Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI)." Malaysian Journal of Applied Sciences 6, no. 1 (2021): 46–56. http://dx.doi.org/10.37231/myjas.2021.6.1.277.
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The years 1997/1998 and 2015/2016 saw the worst El Niño occurrence in human history. The occurrence of El Niño causes extreme temperature events which are higher than usual, drought and prolonged drought. The incident caused a decline in the ability of plants in carrying out the process of photosynthesis. This causes the carbon dioxide content to be higher than normal. Studies on the effects of El Niño and its degree of strength are still under-studied especially by researchers in the tropics. This study uses remote sensing technology that can provide spatial information. The first step of remote sensing data needs to go through the pre-process before building the NDVI (Normalized Difference Vegetation Index) and Normalized Difference Water Index (NDWI) maps. Next this study will identify the relationship between Oceanic Nino Index (ONI) with Application Remote Sensing in The Study Of El Niño Extreme Effect 1997/1998 and 2015/2016 On Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI)NDWI and NDWI landscape indices. Next will make a comparison, statistical and spatial information space between NDWI and NDVI for each year 1997/1998 and 2015/2016. This study is very important in providing spatial information to those responsible in preparing measures in reducing the impact of El Niño.
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Naik, B. Chandrababu, and B. Anuradha. "Extraction of Water-body Area from High-resolution Landsat Imagery." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 6 (2018): 4111–19. https://doi.org/10.11591/ijece.v8i6.pp4111-4119.
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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.
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Naik, B. Chandrababu, and B. Anuradha. "Extraction of Water-body Area from High-resolution Landsat Imagery." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 6 (2018): 4111. http://dx.doi.org/10.11591/ijece.v8i6.pp4111-4119.
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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.
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Slamet, B., O. K. H. Syahputra, H. Kurniawan, M. Saraan, and M. M. Harahap. "Analysis of vegetation cover and built-up areas in the Percut watershed landscape, North Sumatra Province using sentinel-2 imagery." IOP Conference Series: Earth and Environmental Science 912, no. 1 (2021): 012089. http://dx.doi.org/10.1088/1755-1315/912/1/012089.
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Abstract Changes in land cover have an impact on the health condition of a watershed. This research was conducted by utilizing Sentinel-2 imagery for the recording period 2020 and 2021. Three indices were used in this study, namely, the Normalized Difference Built-up Index (NDBI), Normalized Difference Water Index (NDWI) and Normalized Difference Vegetation Index (NDVI). NDBI analysis indicates there is an increase in the built-up area of 2,092.62 hectares which means land conversion. NDWI classification shows an increase in the wetness area of 308.58 hectares, mainly occurring in the downstream part of the watershed, located to the north. There is an increase in the area of non-vegetated areas reaching 288.96 hectares in the Percut watershed based on the results of the NDVI analysis.
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Gulácsi, András, and Ferenc Kovács. "Drought Monitoring With Spectral Indices Calculated From Modis Satellite Images In Hungary." Journal of Environmental Geography 8, no. 3-4 (2015): 11–20. http://dx.doi.org/10.1515/jengeo-2015-0008.
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Abstract In this study a new remote sensing drought index called Difference Drought Index (DDI) was introduced. DDI was calculated from the Terra satellite’s MODIS sensor surface reflectance data using visible red, near-infrared and short-wave-infrared spectral bands. To characterize the biophysical state of vegetation, vegetation and water indices were used from which drought indices can be derived. The following spectral indices were examined: Difference Vegetation Index (DVI), Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Difference Water Index (DWI), Normalized Difference Water Index (NDWI), Difference Drought Index (DDI) and Normalized Difference Drought Index (NDDI). Regression analysis with the Pálfai Drought Index (PaDi) and average annual yield of different crops has proven that the Difference Drought Index is applicable in quantifying drought intensity. However, after comparison with reference data NDWI performed better than the other indices examined in this study. It was also confirmed that the water indices are more sensitive to changes in drought conditions than the vegetation ones. In the future we are planning to monitor drought during growing season using high temporal resolution MODIS data products.
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Sathyaseelan, M., S. K. Ghosh, and C. S. P. Ojha. "ENVIRONMENTAL SUSTAINABILITY ASSESSMENT OF A HIMALAYAN CATCHMENT WITH LAND COVER INDICES AND LST RELATIONSHIP USING PRINCIPAL COMPONENT ANALYSIS – A GEOSPATIAL APPROACH." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-M-1-2023 (April 21, 2023): 285–92. http://dx.doi.org/10.5194/isprs-archives-xlviii-m-1-2023-285-2023.
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Abstract. Environmental sustainability assessment is a crucial part of the management of natural resources. Remote Sensing based environmental land cover indices such as Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Built-up Index (NDBI), Normalized Difference Moisture Index (NDMI), and its associated Land Surface Temperature (LST) are the major governing factors for the environmental processes that happen on the surface of the earth . These NDVI, NDWI, NDBI, NDMI, and LST are generated for 2020 using the Landsat satellite datasets. The process-based relationship among them is complex and involves various parameters but may be easily represented by multiple linear regression models. Principal Component Analysis (PCA) is one such type that efficiently handles and evaluates the contribution of each of these factors to each other based on the sampling units. The study area is the upper Ramganga catchment in the Indian Himalayas, consisting of 117 sub-catchments. These catchment units (samples) are entangled with these environmental factors. The results of the PCA reveal the relationship between each of the environmental factors and their priority. Based on the uncorrelated factors priority suggestion from the PCA, catchment units were classified as high, moderate, or low categories based on their dominance in the relationship among the factors. These spatial variations in the environmental factors can help to assess the sustainability of resources in the Himalayan catchment.
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Filho, Júlio Cezar Cotrim Moreira, and João Rodrigues Tavares Junior. "Avaliação da precisão temática de composições de NDBI, NDVI, NDWI." Revista Brasileira de Geomática 4, no. 1 (2016): 3. http://dx.doi.org/10.3895/rbgeo.v4n1.5462.
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Este artigo trata de experimentos usando composições de índices físicos NDVI (Normalized Difference Vegetation Index), NDWI (Normalized Difference Water Index), NDBI (Normalized Difference Built-Up Index), para avaliar a precisão temática do classificador Máxima Verossimilhança (MAXVER) usando exatidão global, índice kappa e teste Z. A área de estudo foi o entorno da Lagoa Olho D’Água localizada em Jaboatão dos Guararapes-PE. Foram usadas duas cenas TM LANDSAT-5, órbita-ponto 214-066, de 17/03/20111 e 29/09/2011, do DGI-INPE (Divisão de Geração de Imagens do Instituto Nacional de Pesquisas Espaciais), e todo o processamento realizado no SPRING 5.0.6. Os resultados indicam que apenas usando índices físicos substituindo composições RGB, a acurácia temática é muito degradada. A classificação MAXVER da composição NDBI-TM4-TM-3 e IHS obtiveram bons resultados em acurácia temática, demonstrando que o método de combinações de índices físicos e composições RGB podem ser usados para melhorar os resultados da classificação MAXVER.
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& Al-Jibouri, Khalaf. "DETECTION LAND COVER CHANGES OF THE BAQUBA CITY FOR THE PERIOD 2014-2019 USING SPECTRAL INDICES." IRAQI JOURNAL OF AGRICULTURAL SCIENCES 51, no. 3 (2020): 805–15. http://dx.doi.org/10.36103/ijas.v51i3.1036.
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This study was conducted on the Land coverings of the city of Baquba and its outskirts in Diyala province, central Iraq, between latitudes 44º 42ʹ 31.78ʺ ــ 44º33ʹ 14.99ʺ and 33º41ʹ 46.66ʺ ــ 33º 48ʹ 23.18ʺ an area of 180,835 km2. In order to classify the earth covers, it was relied on the field survey to determine the grounding points. Also used two satellite data from Landsat 8, the first one on 23/3/2014, the second on 21/3/2019, and the production of Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI) and Normalized Differences Built- up the Index (NDBI) maps. The results of the survey was showed five varieties are vegetation cover, agricultural land, water, buildings and barren land. They were identified and compared with the 75 land control points, The accuracy of the classification was calculated using Kappa It was 89% , and purely concluded that the use of manuals NDVI, NDWI and NDBI was useful for classifying Land coverings and detecting changes as they are considered an easy and fast method.
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Zhou, Huailin, Guangsheng Zhou, Xingyang Song, and Qijin He. "Dynamic Characteristics of Canopy and Vegetation Water Content during an Entire Maize Growing Season in Relation to Spectral-Based Indices." Remote Sensing 14, no. 3 (2022): 584. http://dx.doi.org/10.3390/rs14030584.
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A variety of spectral vegetation indices (SVIs) have been constructed to monitor crop water stress. However, their abilities to reflect dynamic canopy water content (CWC) and vegetation water content (VWC) during the growing season have not been concurrently examined, and the underlying mechanisms remain unclear, especially in relation to soil drying. In this study, a field experiment was conducted and designed with various irrigation regimes applied during two consecutive growing seasons of maize. The results showed that CWC, VWC, and the SVIs exhibited obvious trends of first increasing and then decreasing within a growing season. In addition, VWC was allometrically related to CWC across the two growing seasons. A linear relationship between the five SVIs and CWC occurred within a certain CWC range (0.01–0.41 kg m−2), while the relationship between these SVIs and VWC was nonlinear. Furthermore, the five SVIs indicated critical values for VWC, and these values were 1.12 and 1.15 kg m−2 for the water index (WI) and normalized difference water index (NDWI), respectively; however, the normalized difference infrared index (NDII), normalized difference vegetation index (NDVI), and optimal soil-adjusted vegetation index (OSAVI) had the same critical value of 0.55 kg m−2. Therefore, in comparison to the NDII, NDVI, and OSAVI, the WI and NDWI better reflected the crop water content based on their sensitives to CWC and VWC. Moreover, CWC was the most important direct biotic driver of the dynamics of SVIs, while leaf area index (LAI) was the most important indirect biotic driver. VWC was a critical indirect regulator of WI, NDWI, NDII, and OSAVI dynamics, whereas vegetation dry mass (VDM) was the critical indirect regulator of NDVI dynamics. These findings may provide additional information for estimating agricultural drought and insights on the impact mechanism of soil water deficits on SVIs.
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Pamungkas, Guntur Bagus. "Analisis Kekeringan Berbasis Remote Sensing dengan Metode Normalized Difference Drought Index (NDDI) secara Multi-Years." REKSABUMI 2, no. 2 (2024): 139–50. http://dx.doi.org/10.33830/reksabumi.v2i2.6494.2023.
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Global drought conditions pose a serious worldwide problem, exacerbated by climate change, deforestation, and excessive water extraction. At the local level, South Tangerang City is grappling with drought challenges due to an extended dry season. Drought mitigation efforts entail the distribution of clean water and public education. The use of innovative remote sensing technology becomes crucial for drought monitoring and management. This study aims to integrate NDVI, NDWI, and NDDI modification methods with multi-year Landsat data to analyse drought in South Tangerang City. The study site focuses on a region characterized by flat to undulating topography and a wet tropical climate. The utilization of Landsat 8 OLI as study data provides comprehensive information through various image bands. NDVI, NDWI, and NDDI analysis methods offer an overview of vegetation conditions, water availability, and drought levels. The results demonstrate a correlation between drought and a decline in vegetation health and water availability. Landsat data from 2008 to 2023 illustrates variations in drought levels in South Tangerang City. This research offers in-depth insights to governments and stakeholders, aiding in the formulation of effective and sustainable drought management strategies at the local level.
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Al-Maliki, Sadiq, Taha I. M. Ibrahim, Gusztáv Jakab, Malihe Masoudi, Jamal S. Makki, and Zoltán Vekerdy. "An Approach for Monitoring and Classifying Marshlands Using Multispectral Remote Sensing Imagery in Arid and Semi-Arid Regions." Water 14, no. 10 (2022): 1523. http://dx.doi.org/10.3390/w14101523.
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Marshlands in arid and semi-arid areas are considered constantly changing environments due to unsecured water supplies as a result of high evapotranspiration and limited and highly variable rainfall. Classification of marshlands in these regions and mapping of their land cover is not an easy task and maps need to be upgraded frequently. Satellites provide enormous amounts of information and data for the continuous monitoring of changes. The aim of this paper is to introduce an approach using multispectral satellite imagery that was adopted to classify and monitor the Al Hammar Marsh (Iraq) over several years and to suggest a relationship between the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Moisture Index (NDMI), and the Normalized Difference Water Index (NDWI), using Landsat 8 data with a resolution of 30 m × 30 m, validated with Sentinel-2 datasets at 10 m × 10 m. Six land cover classes were used: (1) open water, (2) dry area, (3) dense vegetation, (4) medium-density vegetation, (5) sparse vegetation, and (6) wet soil. Three indices, NDWI, NDMI, and NDVI, were chosen for the automatic classification of each pixel and the creation of a time series of land cover maps. The proposed method can efficiently classify and monitor marshlands and can be used to study different marshlands by adjusting the thresholds for NDVI, NDMI, and NDWI. Overall, the correlation for all classes (R) between Landsat 8 and Sentinel-2 is about 0.78. Thus, this approach will help to preserve marshes through improved water management.
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Liang, Jiatan. "Quantifying the multidimensional impacts of extreme weather events on water quality." Water e-Journal 10, no. 4 (2024): 1–3. https://doi.org/10.21139/wej.2024.016.
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With the increasing impact of climate change, extreme weather events such as floods, droughts, wildfires, and heavy rainfall significantly affect inland water quality. However, accurately measuring their intensity, duration, and frequency, as well as quantifying their effects on water quality, remains a challenge. This study employs remote sensing (Sentinel-2, Landsat, MODIS) and GIS tools (ArcMap) to standardise the measurement of extreme weather events and assess their impact on key water quality parameters, including nutrient levels, algal blooms, and sediment load. NDWI (Normalised Difference Water Index) is used to analyze water extent, NDVI (Normalised Difference Vegetation Index) to assess vegetation conditions, and TSM (Total Suspended Matter) to measure sediment load. Additionally, machine learning and statistical models are applied to predict water quality changes. The findings provide a standardised framework for monitoring water resources and improving adaptive strategies in response to climate change.
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SONI, ANIL KUMAR, JAYANT NATH TRIPATHI, KRIPAN GHOSH, M. SATEESH, and PRIYANKA SINGH. "Evaluating crop water stress through satellite-derived crop water stress index (CWSI) in Marathwada region using Google Earth Engine." Journal of Agrometeorology 25, no. 4 (2023): 539–46. http://dx.doi.org/10.54386/jam.v25i4.2211.
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Accurate information of crop water requirements is essential for optimal crop growth and yield. Assessing this information at the appropriate time, particularly during the vegetative and reproductive stages when water demand is highest, is crucial for successful crop production. Our study cantered on the drought-prone Marathwada region, specifically targeting the years 2015 to 2020, encompassing the challenging drought year of 2015 and the favourable year of 2020. The crop water stress was detected using crop water stress (CWSI) index and compared with normalized difference vegetation index (NDVI) and normalized difference wetness index (NDWI) derived from satellite data. Our findings reveal a negative correlation between the CWSI and satellite derived vegetation indices NDVI and NDWI. Notably, the NDWI index exhibits stronger alignment with CWSI compared to NDVI. The correlation demonstrates particular robustness during drought or deficient rainfall years such as 2015, 2017, and 2019, while weaker correlations are observed in 2016, 2018, and 2020. Moreover, these correlations display variations across different areas within distinct rainfall zones.
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Dhaloiya, Arvind, Darshana Duhan, DM Denis, Dharmendra Singh, Mukesh Kumar, and Manender Singh. "Modeling medium resolution evapotranspiration using downscaling techniques in north-western part of India." MAUSAM 74, no. 3 (2023): 561–78. http://dx.doi.org/10.54302/mausam.v74i3.5112.
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The present investigation provides a modeling solution to downscale MODIS-based evapotranspiration (ET) at a 30 m spatial resolution from its original 500 m spatial resolution using meteorological and Landsat 8 (Operational Land Imager, OLI) data by employing downscaling models. The nine indices namely Surface Albedo, Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), Modified Soil-Adjusted Vegetation Index (MSAVI), Normalized Difference Built-up Index (NDBI), Normalized Difference Water Index (NDWI), Normalized Difference Moisture Index (NDMI), and Normalized Difference Infrared Index for Band 7 (NDIIB7) were calculated from Lansat 8 data at 30 m spatial resolution. The multiple linear regression (MLR) and Least Square Support Vector Machine (LS-SVM) models were developed to generate the relationship between MODIS 500 m ET and Landsat indices at 500 m scale. Further, these develop models were used to estimate 30 m ET based on 30 m Landsat 8 indices. The performance of developed models (MLR and LS-SVM) was carried out using correlation coefficient (CC), Nash-Sutcliffe coefficient (NASH) efficiency, Root Mean Square Error (RMSE) and Normalised Mean Square Error (NMSE). Penman–Monteith (PM) method was used to estimate the ET using observed station data. The results show that lowest ETO was observed in the month of December while it was maximum in the month of May. Using the performances indices, it was found that LS-SVM model slightly outperformed than MLR model. However, the downscaled model overestimates ET in comparison to the Penman-Monteith method. Further, the significant correlation was found between MODIS ET and LS-SVM ET at all the stations.
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Artikanur, S. D., Widiatmaka, Y. Setiawan, and Marimin. "Normalized Difference Drought Index (NDDI) computation for mapping drought severity in Bojonegoro Regency, East Java, Indonesia." IOP Conference Series: Earth and Environmental Science 1109, no. 1 (2022): 012027. http://dx.doi.org/10.1088/1755-1315/1109/1/012027.
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Abstract Drought is a natural disaster that causes difficulties meeting household, agriculture, and industrial water needs. Drought often occurs in various regions in Indonesia, one of which is the Bojonegoro Regency. Bojonegoro Regency has the highest number of villages experiencing drought in East Java Province in 2019. This study aims to map the drought severity in the Bojonegoro Regency based on the results of the Normalized Difference Drought Index (NDDI) computation. The method used to obtain NDDI was by subtracting the Normalized Difference Vegetation Index (NDVI) by the Normalized Difference Water Index (NDWI) and then dividing by the NDVI plus the NDWI. The results showed five drought severity classes in Bojonegoro Regency: very low, low, medium, high, and very high. Areas classified as high and very high severity have an area of 1,534.32 (0.66%) and 99.38 ha (0.04%), respectively. These results indicate that many areas have the potential to experience drought in Bojonegoro Regency. The results of this analysis can be an input to the government to carry out mitigation efforts such as building reservoirs and infiltration wells, preserving the karst area, and increasing the vegetation cover against drought disasters in the future.
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Latuamury, B., M. Talaohu, F. Sahusilawane, and W. N. Imlabla. "Correlation of normalized difference water index and baseflow index in small island watershed landscapes." IOP Conference Series: Earth and Environmental Science 883, no. 1 (2021): 012072. http://dx.doi.org/10.1088/1755-1315/883/1/012072.
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Abstract The utilization of remote sensing data in the field of environmental hydrology is experiencing rapid progress. The Normalized Difference Water Index (NDWI) approach to transforming the water content of various land cover types and its implications for small island watersheds' hydrological characteristics is essential. NDWI is an algorithm used to detect water bodies, with the capacity to absorb visible and infrared wavelengths strongly. This study aims to analyze the correlation between the NDWI water index and the BFI baseflow index in the small island landscape of Ambon City. The Landsat 7 ETM + and Landsat 8 OLI image processing methods use ENVI 5.3 software to transform the NDWI algorithm and the BFI + 3.0 digital recursive filtering (RDF) method for hydrological characterization. The results showed that there was a strong correlation between the NDWI water index and the baseflow index (BFI) for the small island watershed of Ambon city. This result is relevant to the geographic area of Ambon City, which is dominated by the ocean 95% and land area 5%, so the application of the NDWI water index and the hydrological conditions of small island watersheds are significant.
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Pan, Hong Jun, Xue Xian Li, Guang Wei Wang, and Chong Song Qi. "Mariculture Zones Extraction Using NDWI and NDVI." Advanced Materials Research 659 (January 2013): 153–55. http://dx.doi.org/10.4028/www.scientific.net/amr.659.153.
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On the analysis of spectral characteristics of Aoshan remote sensing images, we find the spectral differences between mariculture zones and other surface features. This paper combines normalized difference water index with mariculture zones distribution planning to complete the extraction and the statistics of the mariculture zones, in order to effectively achieve the regulation of mariculture zones.
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Burapapol, Kansuma, and Ryota Nagasawa. "Mapping Soil Moisture as an Indicator of Wildfire Risk Using Landsat 8 Images in Sri Lanna National Park, Northern Thailand." Journal of Agricultural Science 8, no. 10 (2016): 107. http://dx.doi.org/10.5539/jas.v8n10p107.
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<p>Severely dry climate plays an important role in the occurrence of wildfires in Thailand. Soil water deficits increase dry conditions, resulting in more intense and longer burning wildfires. The temperature vegetation dryness index (TVDI) and the normalized difference drought index (NDDI) were used to estimate soil moisture during the dry season to explore its use for wildfire risk assessment. The results reveal that the normalized difference wet index (NDWI) and land surface temperature (LST) can be used for TVDI calculation. Scatter plots of both NDWI/LST and the normalized difference vegetation index (NDVI)/LST exhibit the triangular shape typical for the theoretical TVDI. However, the NDWI is more significantly correlated to LST than the NDVI. Linear regression analysis, carried out to extract the maximum and minimum LSTs (LST<sub>max</sub>, LST<sub>min</sub>), indicate that LST<sub>max </sub>andLST<sub>min</sub> delineated by the NDWI better fulfill the collinearity requirement than those defined by the NDVI. Accordingly, the NDWI-LST relationship is better suited to calculate the TVDI. This modified index, called TVDI<sub>NDWI-LST</sub>, was applied together with the NDDI to establish a regression model for soil moisture estimates. The soil moisture model fulfills statistical requirements by achieving 76.65% consistency with the actual soil moisture and estimated soil moisture generated by our model. The relationship between soil moisture estimated from our model and leaf fuel moisture indicates that soil moisture can be used as a complementary dataset to assess wildfire risk, because soil moisture and fuel moisture content (FMC) show the same or similar behavior under dry conditions. <strong></strong></p>
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Bi, L., B. L. Fu, P. Q. Lou, and T. Y. Tang. "DELINEATION WATER OF PEARL RIVER BASIN USING LANDSAT IMAGES FROM GOOGLE EARTH ENGINE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W10 (February 7, 2020): 5–10. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w10-5-2020.
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Abstract. Surface water plays an important role in ecological circulation. Global climate change and urbanization affect the distribution and quality of water. In order to obtain surface water information quickly and accurately, this study uses Google Earth Engine (GEE) as a data processing tool, 309 Landsat 8 series images from 2016 to 2019 are selected to calculate 4 different water indexes, including Normalized Difference Water Index (NDWI), Modified NDWI (MNDWI), Automated Water Extraction Index (AWEIsh) and Multi- Band Water Index (MBWI) to extract surface water in Pearl River Basin. In order to remove the influence of other ground objects, Normalized Vegetation Index (NDVI), Normalized Difference Building Index (NDBI) and Digital Surface Model (DSM) are combined with the above four water indexes, and threshold segmentation is used to eliminate the influence of vegetation, buildings and mountains. Finally, take the advantage of morphological filtering algorithm to eliminate non-water pixels. The results show that GEE is able to extract surface water in a very short time; AWEIsh has the highest overall accuracy of 94.12%, which is 7.20% higher than the classical NDWI method; There is no significant difference in the width and shape of rivers from 2015 to 2018; The locations of the rivers extracted by the four methods are consistent with the 1 : 100,000 river system basic data of 2015 provided by the Ministry of Water Resources of the People’s Republic of China.
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Acharya, Tri, Anoj Subedi, and Dong Lee. "Evaluation of Water Indices for Surface Water Extraction in a Landsat 8 Scene of Nepal." Sensors 18, no. 8 (2018): 2580. http://dx.doi.org/10.3390/s18082580.
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Accurate and frequent updates of surface water have been made possible by remote sensing technology. Index methods are mostly used for surface water estimation which separates the water from the background based on a threshold value. Generally, the threshold is a fixed value, but can be challenging in the case of environmental noise, such as shadow, forest, built-up areas, snow, and clouds. One such challenging scene can be found in Nepal where no such evaluation has been done. Taking that in consideration, this study evaluates the performance of the most widely used water indices: Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Modified NDWI (MNDWI), and Automated Water Extraction Index (AWEI) in a Landsat 8 scene of Nepal. The scene, ranging from 60 m to 8848 m, contains various types of water bodies found in Nepal with different forms of environmental noise. The evaluation was conducted based on measures from a confusion matrix derived using validation points. Comparing visually and quantitatively, not a single method was able to extract surface water in the entire scene with better accuracy. Upon selecting optimum thresholds, the overall accuracy (OA) and kappa coefficient (kappa) was improved, but not satisfactory. NDVI and NDWI showed better results for only pure water pixels, whereas MNDWI and AWEI were unable to reject snow cover and shadows. Combining NDVI with NDWI and AWEI with shadow improved the accuracy but inherited the NDWI and AWEI characteristics. Segmenting the test scene with elevations above and below 665 m, and using NDVI and NDWI for detecting water, resulted in an OA of 0.9638 and kappa of 0.8979. The accuracy can be further improved with a smaller interval of categorical characteristics in one or multiple scenes.
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Liuzzo, Lorena, Valeria Puleo, Salvatore Nizza, and Gabriele Freni. "Parameterization of a Bayesian Normalized Difference Water Index for Surface Water Detection." Geosciences 10, no. 7 (2020): 260. http://dx.doi.org/10.3390/geosciences10070260.
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The normalized difference water index (NDWI) has been extensively used for different purposes, such as delineating and mapping surface water bodies and monitoring floods. However, the assessment of this index (based on multispectral remote sensing data) is highly affected by the effects of atmospheric aerosol scattering and built-up land, especially when green and near infrared bands are used. In this study, a modified version of the NDWI was developed to improve precision and reliability in the detection of water reservoirs from satellite images. The proposed equation includes eight different parameters. A Bayesian procedure was implemented for the identification of the optimal set of these parameters. The calculation of the index was based on Sentinel-2 satellite images of spectral bands collected over the 2015–2019 period. The modified NDWI was tested for the identification of small reservoirs in a subbasin of the Belice catchment in Sicily (southern Italy). To assess the effectiveness of the index, a reference image, representing the actual reservoirs in the study area, was used. The results suggested that the use of the proposed methodology for the parameterization of the modified NDWI improves the identification of water reservoirs with surfaces smaller than 0.1 ha.
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Husin, Rizal, Patrich E. Ph Papilaya, and Bokiraiya Latuamury. "Karakteristik Indeks Air Menggunakan Normalized Difference Water Index (NDWI) Pada DAS Negeri Rutong Kota Ambon." Jurnal Geografi, Lingkungan dan Kesehatan 2, no. 1 (2024): 13–29. http://dx.doi.org/10.30598/jglk.2.1.13623.
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The objectives of this study are analyzing the transformation of the water content of the NDWI index and the spatial pattern of its mapping in the Rutong State Watershed of Ambon City and analyzing the influence of environmental attributes on spatial patterns of NDWI index water content. The results of the transformation of the NDWI index water content and the distribution pattern of the NDWI water content for the 2010-2022 period show that changes in the distribution of NDWI water content have shifted ways that extend to coastal areas towards the mainland. The difference between the watery area and the dry area around it is visible. High water content dominates with the water content of tree vegetation begins to experience decreasing changes. In contrast, low and moderate water content increases and spreads along the coast, moving towards land areas. The spatial distribution pattern of low and moderate NDWI water content extends from coastal and inland areas to hills and mountains. The results of the analysis of the influence of environmental attributes on the spatial pattern of the NDWI index water content showed that the results of the ANOVA F-count (36,051) were more significant than the F-table (2,479), indicating that the parameters of temperature, humidity, vegetation density and type of vegetation cover had a considerable influence simultaneously on the water content of the NDWI index at a significance level (0.000) smaller than α 0.05. The results of the partial test (t-test) show that the parameters of temperature, humidity, and vegetation density have a partially significant influence on the water content of the NDWI index. While the variable type of vegetation cover has an insignificant impact on water content, the NDWI index is at a significance level of α 0.05.
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Nagy, Attila, Nikolett Éva Kiss, Erika Buday-Bódi, et al. "Precision Estimation of Crop Coefficient for Maize Cultivation Using High-Resolution Satellite Imagery to Enhance Evapotranspiration Assessment in Agriculture." Plants 13, no. 9 (2024): 1212. http://dx.doi.org/10.3390/plants13091212.
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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.
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Marusig, Daniel, Francesco Petruzzellis, Martina Tomasella, Rossella Napolitano, Alfredo Altobelli, and Andrea Nardini. "Correlation of Field-Measured and Remotely Sensed Plant Water Status as a Tool to Monitor the Risk of Drought-Induced Forest Decline." Forests 11, no. 1 (2020): 77. http://dx.doi.org/10.3390/f11010077.
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Increased frequency of tree mortality and forest decline due to anomalous drought events calls for the adoption of effective monitoring of tree water status over large spatial and temporal scales. We correlated field-measured and remotely sensed plant water status parameters, to test the possibility of monitoring the risk of drought-induced dehydration and hydraulic failure using satellite images calibrated on reliable physiological indicators of tree hydraulics. The study was conducted during summer 2019 in the Karst plateau (NE Italy) in a woodland dominated by Fraxinus ornus L.; Sentinel-2 images were acquired on a seasonal scale on the same dates when absolute water content (AbWC), relative water content (RWC), and minimum water potential (Ψmin) were measured in the field. Plant water status parameters were correlated with normalized difference vegetation index (NDVI and NDVI 8A), normalized difference water index (NDWI), and soil-adjusted vegetation index (SAVI). Significant Pearson and Spearman linear correlations (α < 0.05) emerged between all tree-level measured variables and NDWI, while for NDVI, NDVI 8A, and SAVI no correlation was found. Our results suggest the possibility of using the NDWI as a proxy of tree water content and water potential.
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Prihantono, J., N. S. Adi, T. Nakamura, and K. Nadaoka. "The Impact of Groundwater Variability on Mangrove Greenness in Karimunjawa National Park based on Remote Sensing Study." IOP Conference Series: Earth and Environmental Science 925, no. 1 (2021): 012064. http://dx.doi.org/10.1088/1755-1315/925/1/012064.
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Abstract This study aims to understand the impact of groundwater table on soil moisture and mangrove greenness in different seasons in Karimunjawa National Park (KNP). We used Sentinel-2 L2A satellite imagery, Global Precipitation Measurement (GPM) satellite rainfall data, and water table observations at KNP. This study estimates Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) on time series Sentinel-2 imagery in 2019-2020 using Google Earth Engine. In addition, we compared the monthly average rainfall data, the monthly average water table data, and the monthly average NDVI, NDWI data extracted at the water table observation points. NDVI is a method to estimate mangrove greenness, and NDWI to estimate soil moisture. The obtained results indicate that NDVI and NDWI in the near shoreline area show a higher value than in the middle area of the KNP that is far from the shoreline. In addition, the value of the NDVI and NDWI correlation coefficients is 0.94, which indicates a positive and strong correlation. Moreover, The NDWI and water table correlation coefficients are 0.79, which indicates a relatively strong positive correlation. Furthermore, the correlation between rainfall and the water table is 0.61, which indicates a relatively strong positive correlation. Thus, these findings show that the water table influences soil moisture and then affects the mangrove greenness. Besides that, the water table change is governed by rainfall, and therefore, the mangrove greenness in KNP depends on seasons and is vulnerable to drought.
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Arenas, Carlos, and Víctor Gudiño. "Propuesta metodológica para la teledetección de la zona estuarina del humedal del Río Limarí, sitio RAMSAR, Región de Coquimbo, Chile." Revista de Biología Marina y Oceanografía 59, no. 3 (2024): 183–97. https://doi.org/10.22370/rbmo.2024.59.3.4847.
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Se presentan los resultados preliminares de un procedimiento estandarizado para delimitar la zona estuarina mediante un árbol de decisiones sobre umbrales de histograma tomando como objeto de estudio el humedal del Río Limarí, un sitio RAMSAR, usando los índices satelitales NDVI (Normalized Difference Vegetation Index), NDWI (Normalized Difference Water Index), NDMI (Normalized Difference Moisture Index), VSSI (Vegetation Soil Salinity Index) y SWI (Salty Water Index); los que identificaron sus componentes vegetacionales, hidrológicos y sedimentarios, aportando desde el 85,27% al 38,04% de los píxeles en la solución final, en orden decreciente. Los resultados se optimizaron mediante un filtro ráster y selección vectorial, ofreciendo una nueva herramienta para la delimitación y discriminación del estuario bajo y la zona fluvial de este humedal.
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Wesson, Cameron, and Wilma Britz. "Using remote sensing to assess plant health and drought response in game reserves and adjacent farmland overtime in the Eastern Cape, South Africa." South African Journal of Geomatics 10, no. 2 (2022): 223–37. http://dx.doi.org/10.4314/sajg.v10i2.15.
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The aim of the study described in this article was to investigate the vegetation health and drought response of naturally occurring Albany thicket and neighbouring farmland vegetation, that appears in an area of the Eastern Cape, South Africa. Google Earth Engine was used to manipulate Landsat 5, 7 and 8 datasets to produce a 30-year temporal dataset, after which the normalized difference vegetation index (NDVI) and the normalized difference water index (NDWI) were then applied to create a time series analysis. The Mann-Kendall and Spearman correlation statistical tests were used on the time series to observe trends and correlations between the NDVI and the NDWI datasets. The Spearman correlation test results showed that there were high correlations between the NDVI and the NDWI datasets (all above 0.805). Furthermore, the Man-Kendall test showed that all the datasets had positively increasing trends, while the NDVI datasets all had monotonic positive trends. Large differences in the NDVI and the NDWI were seen for the different vegetation types during times of drought, and farmland was the most severely affected with an average of 19% decrease in the NDVI and an average of 71% decrease in the NDWI.
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Trinh, Le Hung, Thi Giang Le, Xuan Bien Tran, Quoc Vinh Tran, Van Phu Le, and Thi Phuong To. "Monitoring of coastline change using Sentinel-2 MSI data. A case study in Thanh Hoa Province, Vietnam." Bulletin of Geography. Physical Geography Series, no. 26 (June 25, 2024): 77–87. http://dx.doi.org/10.12775/bgeo-2024-0006.
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Vietnam is a coastal country with a coastline of more than 3,260 km, stretching from north to south. Coastal change in Vietnam is complicated further by the effects of climate change, including erosion and accretion, causing great impacts on infrastructure and the environment. This article presents the results of assessing coastline changes in Thanh Hoa Province (North Central region of Vietnam) from Sentinel-2 MSI satellite image data for the period 2015–23. Three Sentinel-2 MSI images taken in December 2015, December 2020 and December 2023 were used to calculate the water indices, including Normalised Difference Water Index (NDWI), Modified Normalised Difference Water Index (MNDWI) Argumented Normalised Difference Water Index (ANDWI) and Automated Water Extraction Index (AWEIsh), then extract the shoreline using the thresholding method and select the water index with the highest accuracy through comparing the overall accuracy and Kappa coefficient. The coastlines of 2015, 2020 and 2023 years are overlaid to evaluate the coastal changes in the study area. The results received in the study provide objective and timely information, helping managers effectively monitor and respond to coastline changes.
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Apshikur, B., M. Ye Rakhymberdina, A. K. Kapasov, M. M. Toguzova, and V. P. Kolpakova. "INVESTIGATION OF THE PROCESSES OF ECOLOGICAL AND ECOSYSTEM CHANGES IN WATER BODIES USING UAV DATA." Bulletin D. Serikbayev of EKTU, no. 1 (March 2024): 36–47. http://dx.doi.org/10.51885/1561-4212_2024_1_36.
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In recent years, the water landscapes of inland rivers have been subjected to significant an- thropogenic impacts exceeding the limits of their self-healing ability. Traditional methods of monitoring ge- oecological parameters - sampling of water in the field with subsequent laboratory analysis - require signif- icant financial and time costs. Traditional methods allow assessing the ecological state of the entire water- course not spatially, but pointwise. In this research, sensors on board the UAV measured solar radiation reflected from the surface of the water and used image data from different camera ranges. The combina- tion of images used was based on a common methodology for identifying key features, and a geographic information system (GIS) modelling approach was used for spatially analyze the distribution and interaction of environmental features. In addition, research the ecological condition of the Yertis River and the quality of water discharged after the municipal wastewater treatment facilities of Ust-Kamenogorsk city into the Yertis River, the data from the DJI Phantom 4 RTK/multispectral drone was processed and the normalized vegetation index NDVI, normalised water level difference index NDWI, water chromaticness index CI, tur- bidity index NDTI and chlorophyll concentration index "a" were calculated. The results obtained can serve as a basis for scientific analysis of the environmental conditions of the researching area, including for learning the processes of eutrophication and other changes in the environment.
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Prasuna Rani, P., M. Sunil Kumar, and P. V. Geetha Sireesha. "Mapping of active and empty aquaponds using spectral indices in coastal region of Guntur District, Andhra Pradesh, India." Journal of Environmental Biology 42, no. 5 (2021): 1338–46. http://dx.doi.org/10.22438/jeb/42/5/mrn-1634.
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Aim: To evaluate spectral indices as tools for separation of active aquaponds filled with water and engaged in shrimp/fish production from empty aquaponds using Landsat -8 data in coastal region of Guntur district, Andhra Pradesh. Methodology: The active and empty aquaponds were demarcated with Landsat satellite (Landsat-8) Operational Land Imager’s (OLI) multispectral images using maximum likelihood classifier (MLC) algorithm and spectral indices like Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Modified NDWI (MNDWI), Water Ratio Index (WRI) and Automated Water Extraction Index (AWEInsh) by means of thresholds. Results: The supervised classification using maximum likelyhood classifier recorded the highest active aquapond area whereas; NDWI, combination of indices and WRI resulted in lower but almost similar extents. Evaluation of confusion matrix using validation points revealed that NDWI, WRI and combination of indices resulted in all most perfect agreement with a kappa value of more than 0.9. Maximum likelihood classifier, NDVI and MNDWI could separate active ponds and empty ponds from other land uses with strong agreement, while AWEInsh could separate different land uses only with moderate agreement. Interpretation: The study indicates that spectral indices like NDWI, WRI and combination of indices are able to delineate aquaponds that were cultured for shrimp/fish and kept empty at a given time with noticeably high accuracy using satellite data for better managing of resources in coastal ecosystem.
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Prasuna Rani, P., M. Sunil Kumar, and P. V. Geetha Sireesha. "Mapping of active and empty aquaponds using spectral indices in coastal region of Guntur District, Andhra Pradesh, India." Journal of Environmental Biology 42, no. 5 (2021): 1338–46. http://dx.doi.org/10.22438/jeb/42/5/mrn-1634.
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Aim: To evaluate spectral indices as tools for separation of active aquaponds filled with water and engaged in shrimp/fish production from empty aquaponds using Landsat -8 data in coastal region of Guntur district, Andhra Pradesh. Methodology: The active and empty aquaponds were demarcated with Landsat satellite (Landsat-8) Operational Land Imager’s (OLI) multispectral images using maximum likelihood classifier (MLC) algorithm and spectral indices like Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Modified NDWI (MNDWI), Water Ratio Index (WRI) and Automated Water Extraction Index (AWEInsh) by means of thresholds. Results: The supervised classification using maximum likelyhood classifier recorded the highest active aquapond area whereas; NDWI, combination of indices and WRI resulted in lower but almost similar extents. Evaluation of confusion matrix using validation points revealed that NDWI, WRI and combination of indices resulted in all most perfect agreement with a kappa value of more than 0.9. Maximum likelihood classifier, NDVI and MNDWI could separate active ponds and empty ponds from other land uses with strong agreement, while AWEInsh could separate different land uses only with moderate agreement. Interpretation: The study indicates that spectral indices like NDWI, WRI and combination of indices are able to delineate aquaponds that were cultured for shrimp/fish and kept empty at a given time with noticeably high accuracy using satellite data for better managing of resources in coastal ecosystem.
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Al-Huqail, Asma A., and Zubairul Islam. "Ecological Stress Assessment on Vegetation in the Al-Baha Highlands, Saudi Arabia (1991–2023)." Sustainability 17, no. 7 (2025): 2854. https://doi.org/10.3390/su17072854.
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Climate change significantly stresses cold-adapted and stenothermic plant species in high-altitude mountain ecosystems. The diverse plant species at elevations ranging from 1324 to 2527 m above mean sea level (AMSL) provide an ideal setting for investigating these impacts in the Al-Baha Highlands, Saudi Arabia. Therefore, this study has three aims: first, to estimate vegetation cover in 2023 and its relationship with environmental factors; second, to analyze long-term trends (1991–2023) in key spectral indices, including the normalized difference vegetation index (NDVI), normalized difference vegetation water index (NDWI), normalized difference open water index (NDWIw), and land surface temperature (LST), using the Kendall tau-b method; and third, to model ecological stress via a generalized additive model (GAM) and assess its impact on vegetation. We utilized Landsat 5/7/8 (C2 SR T1) for spectral indices and the Copernicus DEM for topographic and hydrological analysis. The results indicate significant roles of LST, elevation, and distance from seasonal streams in shaping vegetation patterns (p < 2 × 10−16). There were negative trends in the NDVI (91.66 km2), NDWI (138 km2), and NDWIw (804 km2) (p < 0.05), whereas the LST exhibited positive trends (116.15 km2) (p < 0.05). The GAM achieved high predictive accuracy (R2 = 0.979), capturing nonlinear relationships between the predictors and the stress score. Severe ecological stress occurred in high-altitude zones (>1700 m AMSL) on south-facing slopes due to increased LST and declining NDWI, impacting species such as Juniperus procera. Hypothesis testing was used to assess variations in the NDVI, its long-term trends, and ecological stress between highland and lower-elevation areas, revealing highly significant differences (p < 2.2 × 10−16). This study provides novel insights into ecological stress dynamics in relation to altitude and slope aspects, offering actionable recommendations for sustainable ecosystem management, including targeted reforestation and water resource optimization to mitigate stress and preserve biodiversity.
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Sulma, Sayidah, Jalu Tejo Nugroho, Any Zubaidah, Hana Listi Fitriana, and Nanik Suryo Haryani. "DETECTION OF GREEN OPEN SPACE USING COMBINATION INDEX OF LANDSAT 8 DATA (CASE STUDY: DKI JAKARTA)." International Journal of Remote Sensing and Earth Sciences (IJReSES) 13, no. 1 (2017): 1. http://dx.doi.org/10.30536/j.ijreses.2016.v13.a2712.
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Spatial information about the availability and presence of green open space in urban areas to be up to date and transparent was a necessity. This study explained the technique to get the green open spaces of spatial information quickly using an index approach of Landsat 8. The purpose of this study was to evaluate the ability of the method to detect the green open spaces, especially using Landsat 8 with a combination of several indices, namely Normalized Difference Build-up Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Build-up Index (NDBI) and Normalized Difference Bareness Index (NDBaI) with a study area of Jakarta. This study found that the detection and identification of green open space classes used a combination of index and band gave good results with an accuracy of 81%.
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Ginal, Philipp, Francisco D. Moreira, Raquel Marques, Rui Rebelo, and Dennis Rödder. "Predicting terrestrial dispersal corridors of the invasive African clawed frog Xenopus laevis in Portugal." NeoBiota 64 (January 28, 2021): 103–18. http://dx.doi.org/10.3897/neobiota.64.60004.
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Invasive species, such as the mainly aquatic African clawed frog Xenopus laevis, are a main threat to global biodiversity. The identification of dispersal corridors is necessary to restrict further expansion of these species and help to elaborate management plans for their control and eradication. Here we use remote sensing derived resistance surfaces, based on the normalised difference vegetation index (NDVI) and the normalised difference water index (NDWI) accounting for behavioural and physiological dispersal limitations of the species, in combination with elevation layers, to determine fine scale dispersal patterns of invasive populations of X. laevis in Portugal, where the frog had established populations in two rivers. We reconstruct past dispersal routes between these two invaded rivers and highlight high risk areas for future expansion. Our models suggest terrestrial dispersal corridors that connect both invaded rivers and identify artificial water bodies as stepping stones for overland movement of X. laevis. Additionally, we found several potential stepping stones into novel areas and provide concrete information for invasive species management.
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Ginal, Philipp, Francisco D. Moreira, Raquel Marques, Rui Rebelo, and Dennis Rödder. "Predicting terrestrial dispersal corridors of the invasive African clawed frog Xenopus laevis in Portugal." NeoBiota 64 (January 28, 2021): 103–18. https://doi.org/10.3897/neobiota.64.60004.
Full textAbstract:
Invasive species, such as the mainly aquatic African clawed frog Xenopus laevis, are a main threat to global biodiversity. The identification of dispersal corridors is necessary to restrict further expansion of these species and help to elaborate management plans for their control and eradication. Here we use remote sensing derived resistance surfaces, based on the normalised difference vegetation index (NDVI) and the normalised difference water index (NDWI) accounting for behavioural and physiological dispersal limitations of the species, in combination with elevation layers, to determine fine scale dispersal patterns of invasive populations of X. laevis in Portugal, where the frog had established populations in two rivers. We reconstruct past dispersal routes between these two invaded rivers and highlight high risk areas for future expansion. Our models suggest terrestrial dispersal corridors that connect both invaded rivers and identify artificial water bodies as stepping stones for overland movement of X. laevis. Additionally, we found several potential stepping stones into novel areas and provide concrete information for invasive species management.
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Sima, Lei, Yisha Liu, Jian Zhang, and Xiaowei Shang. "Research on Summer Hourly Climate-Influencing Factors in Suburban Areas of Cities in CFA Zone—Taking Chengdu, China as an Example." Buildings 14, no. 10 (2024): 3083. http://dx.doi.org/10.3390/buildings14103083.
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Elevated temperatures in urban centers have become a common problem in cities around the world. However, the climate problems in suburban areas are equally severe; there is an urgent need to find zero-carbon ways to mitigate this problem. Recent studies have revealed the thermal performance of vegetation, buildings, and water surfaces. They functioned differently regarding the climate at different periods of the day. Accordingly, this study synthesizes remote sensing technology and meteorology station observation data to deeply explore the differences in the role of each climate-influencing factor in the suburban areas of Chengdu. The land surface temperature (LST) and air temperature (Ta) were used as thermal environmental indicators, while the normalized difference vegetation index (NDVI), normalized difference water index (NDWI), normalized difference built-up index (NDBI), and altitude were used as environmental factors. The results showed that the relevant influences of the environmental factors on the climate in the sample areas were significantly affected by the time of the day. The NDVI (R2 = 0.5884), NDBI (R2 = 0.3012), and altitude (R2 = 0.5638) all showed strong correlations with Ta during the night (20:00–7:00), which gradually weakened after sunrise, yet the NDWI showed a poorer cooling effect during the night, which gradually strengthened after sunrise, reaching a maximum at 15:00 (R2 = 0.5012). One reason for this phenomenon was the daily weather changes. These findings facilitate the advancement of the understanding of the climate in suburban areas and provide clear directions for further thermal services targeted towards people in different urban areas.
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Hurat, Ismael Abbas. "Analysis on Thermal Islands Effectors in Ramadi City, Iraq Using Multi-temporal Landsat Images." Al-Adab Journal 1, no. 135 (2020): 67–78. http://dx.doi.org/10.31973/aj.v1i135.983.
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This paper analyzes the effects of urban density, vegetation cover, and water body on thermal islands measured by land surface temperature in Al Anbar province, Iraq using multi-temporal Landsat images. Images from Landsat 7 ETM and Landsat 8 OLI for the years 2000, 2014, and 2018 were collected, pre-processed, and anal yzed. The results suggested that the strongest correlation was found between the Normalized Difference Built-up Index (NDBI) and the surface temperature. The correlation between the Normalized Difference Vegetation Index (NDVI) and the surface temperature was slightly weaker compared to that of NDBI. However, the weakest correlation was found between the Normalized Difference Water Index (NDWI) and the temperature. The results obtained in this research may help the decision makers to take actions to reduce the effects of thermal islands by looking at the details in the produced maps and the analyzed values of these spectral indices.
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Langat, Philip Kibet, Manoj Kumer Ghosh, Chandan Roy, Puspita Talukdar, Richard Koech, and Arjun Neupane. "Mapping Coastal Dynamics Induced Land Use Change in Sandwip Island, Bangladesh." Remote Sensing 16, no. 24 (2024): 4686. https://doi.org/10.3390/rs16244686.
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Evaluating satellite water extraction indices, particularly for coastal environments, guarantees that satellite-derived water maps are as accurate and functional as possible, notwithstanding the unique complexities these areas present. Variability in salinity levels, intricate land-water boundaries, dynamic sediment loads, and tidal fluctuations often complicate coastal water mapping. Sandwip Island in Bangladesh is one of the most complex and dynamic coastal environments in the world and is our area of focus. Six water information extraction indices were evaluated: normalized-difference vegetation index (NDVI), modified normalized difference water index (MNDWI), automated water extraction index for built-up areas (AWEInsh) and shadows (AWEIsh), multi-band water index (MBWI), and normalized difference water index (NDWI), using Sandwip Island’s satellite Landsat imagery acquired in February 1990, 2000, 2010, and 2020. The results showed that NDWI performed the best based on the total area obtained and classification accuracy. NDWI was then used to assess the erosion and accretion dynamics of the island for the study period (1990–2020). In the period 1990–2000, the island saw significant erosion and accretion along its coastlines in all parts, while the 2000–2010 period indicated that the island eroded on all sides. However, the situation was totally opposite during 2010–2020. The results illustrated the best performance of the NDWI algorithm in mapping surface water in the complex and dynamic Sandwip coastal environment. Also, erosion and accretion change temporally and spatially on the island. While this study is confined to Sandwip Island in Bangladesh, the findings hold the potential for broader applicability in regions with comparable characteristics.
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Xu, Hanqiu. "Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery." International Journal of Remote Sensing 27, no. 14 (2006): 3025–33. http://dx.doi.org/10.1080/01431160600589179.
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Li, Chuan, Iman Rousta, Haraldur Olafsson, and Hao Zhang. "Lake Water Quality and Dynamics Assessment during 1990–2020 (A Case Study: Chao Lake, China)." Atmosphere 14, no. 2 (2023): 382. http://dx.doi.org/10.3390/atmos14020382.
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Settlements along the coastlines of oceans and lakes, which are among the world’s most densely populated areas, are in immediate danger due to stressors brought on by climate change and dangers posed by human activities. This study investigates the water changes of Chao Lake during the last 30 years by using Landsat 5, 7 and 8 time-series images and water indices, including Normalized Difference Water Index (NDWI), Normalized Difference Turbidity Index (NDTI), Green Normalized Difference Vegetation Index (GNDVI) and Normalized Sea Surface Temperature (SST). The gathered data demonstrates that each estimated indicator’s value has increased with time. Thus, over the course of the 30-year research period, the NDWI, NDTI, GNDVI and SST annual average values show increases of 112.10%, 242.42%, 112.82% and 119.42%, respectively. The NDWI index underwent these fluctuations, evidenced with the biggest amount (681.8%) in the winter and the lowest amount (28.13%) in the fall. The most NDTI changes (480%) and the least (only 50%) occurred in summer and fall, respectively. The largest increases in GNDVI (180%) and SST values (537.86%) were observed in winter; the smallest changes in GNDVI (43.48%) and GNDVI (68.76%) in fall. The outcomes also demonstrated a strong link between all four estimated factors. In the majority of the analyzed months, the correlation between the 2 measures, GNDVI and NDTI, was considerably greater and near to 1. The findings of this study may be utilized by managers, decision-makers and local planners for the purpose of environmental planning and reducing water pollution in Chao Lake (and other water regions), as well as reducing the risk of environmental hazards due to water pollution.
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Kotlarz, Jan. "Wpływ susz na wskaźniki teledetekcyjne grądu wysokiego i boru mieszanego w Lesie Młochowskim – analiza zobrazowań satelitarnych Sentinel-2 lasów objętych ochroną ścisłą oraz gospodarczych w latach 2017–2021." Leśne Prace Badawcze 82, no. 3 (2023): 87–100. https://doi.org/10.48538/lpb-2021-0010.
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The purpose of this paper was to describe processes that took place in the Łowicz-Błonia plain during the long-term drought of 2018 and the series of short-term droughts in 2019. For our analysis we used multispectral satellite images of highground hornbeam and mixed coniferous forest in the Młochowski Forest from 2017–2021. Sentinel-2 images provided the means to investigate the impact of mild droughts on the values of the NDVI (Normalized Difference Vegetation Index), NDWI (Normalized Difference Water Index), and MSI (Moisture Stress Index) as well as their monthly variability and differences between forest divisions. During periods without drought, the variability of all three indices was typical for each phase of the vegetation cycle: in the spring months the value of NDVI and MSI increased, NDWI decreased. During the autumn months, the behavior of the indicators reversed. In the period of long-term drought in 2018, the NDWI was higher in forest divisions with a species composition characteristic of a mixed coniferous forest compared to divisions with a higher share of deciduous trees such as oaks and hornbeams, including the rigorously protected area of high–hornbeam forest. NDWI was the only index to show a downward trend during mild droughts, while during moderate droughts, also a decrease in NDVI and MSI was observed. This was most clearly seen in deciduous forests. We did not observed any correlation of NDVI, NDWI, or MSI with the protection status of the forest or the absence thereof.
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Khatri, Bishal, and Rakshya Khatri. "Assessment on the Relationship of Spectral Indices With Land Surface Temperature Using Google Earth Engine: A Case Study of Chitwan District, Nepal." Advances in Geomatics 2, no. 2 (2024): 74–87. https://doi.org/10.5281/zenodo.14555495.
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Land surface temperature (LST) is a significant characteristic that influences the Earth's climate system and the flow of energy between land and atmosphere. Land cover variations, as measured by spectral indices, have a substantial influence on LST. This study uses Google Earth Engine (GEE) to analyze the link between LST and several spectral indices in Nepal's Chitwan area. Chitwan was chosen for its diversified scenery, which includes thick forest within Chitwan National Park, permanent water bodies, urban areas, and continuous urbanization with rising temperatures. Landsat 8 data were used to generate the Normalized Difference Built-up Index (NDBI), Normalized Difference Water Index (NDWI), Enhanced Vegetation Index (EVI), Normalized Difference Bareness Index (NDBaI), and LST. All data collecting, processing, and extraction were done using the GEE platform. The spatial analysis discovered considerable changes in LST across the district, ranging from 18.86°C in vegetated regions to 40.69°C in urban centers and bare fields. The distribution of spectral indices revealed additional information: high EVI values indicated healthy vegetation, whereas high NDBI values linked to built-up regions. The NDWI successfully mapped water bodies, whereas NDBaI found regions with little vegetative cover. Pearson's correlation analysis supported the predicted relationships: lower LST with vegetation and water (EVI, NDWI: negative correlation), and higher LST with built-up areas (NDBI: positive correlation). This research emphasizes the use of remote sensing for analyzing land cover-LST interactions, as well as the role of plant cover in moderating urban heat island impacts.
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Lingga, Meryvita Karla Anju, Triyana Muliawati, and Danni Gathot Harbowo. "Analisis Kombinasi Spectral Band Value Kawasan Restorasi Savana di Taman Nasional Baluran." PROSIDING SEMINAR NASIONAL SAINS DATA 4, no. 1 (2024): 498–508. https://doi.org/10.33005/senada.v4i1.270.
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Kekeringan merupakan fenomena alam yang dapat berdampak negatif pada lingkungan, kehidupan manusia, dan ekonomi. Fenomena ini melibatkan berbagai kondisi seperti kondisi atmosfer, curah hujan, ketersediaan air tanah, serta dampaknya terhadap vegetasi dan kehidupan hewan. Dalam penelitian ini, analisis kekeringan dilakukan di Taman Nasional Baluran yang terbagi menjadi empat area: Gunung Baluran, Evergreen Forest, Savana Bekol, dan Pantai Bama. Parameter yang digunakan adalah kombinasi spectral band value termasuk NDVI (Normalized Difference Vegetation Index), NDWI (Normalized Difference Water Index), MNDWI (Modified Normalized Difference Water Index), NDMI (Normalized Difference Moisture Index), dan SAVI (Soil Adjusted Vegetation Index). Teknologi citra satelit sentinel 2A digunakan untuk analisis, serta metode regresi dan PCA (Principal Component Analysis). Hasil penelitian menunjukkan bahwa Gunung Baluran dan Evergreen Forest memiliki vegetasi yang baik, kelembaban tanah tinggi, dan tutupan kanopi yang menunjukkan kawasan berhutan. Sebaliknya, Savana Bekol memiliki karakteristik yang berbeda. Pantai Bama memiliki kebasahan air asin yang tinggi dan merupakan daerah lautan.