Academic literature on the topic 'Normalised Difference Water Index (NDWI)'
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Journal articles on the topic "Normalised Difference Water Index (NDWI)"
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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.
Dissertations / Theses on the topic "Normalised Difference Water Index (NDWI)"
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Andersson, Jafet. "Land Cover Change in the Okavango River Basin : Historical changes during the Angolan civil war, contributing causes and effects on water quality." Thesis, Linköping University, Department of Water and Environmental Studies, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-7152.
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<p>The Okavango river flows from southern Angola, through the Kavango region of Namibia and into the Okavango Delta in Botswana. The recent peace in Angola hopefully marks the end of the intense suffering that the peoples of the river basin have endured, and the beginning of sustainable decision-making in the area. Informed decision-making however requires knowledge; and there is a need for, and a lack of knowledge regarding basin-wide land cover (LC) changes, and their causes, during the Angolan civil war in the basin. Furthermore, there is a need for, and a lack of knowledge on how expanding large-scale agriculture and urban growth along the Angola-Namibia border affects the water quality of the river.</p><p>The aim of this study was therefore to develop a remote sensing method applicable to the basin (with scant ground-truth data availability) to carry out a systematic historic study of LC changes during the Angolan civil war, to apply the method to the basin, to relate these changes to major societal trends in the region, and to analyse potential impacts of expanding large-scale agriculture and urban growth on the water quality of the river along the Angola-Namibia border.</p><p>A range of remote sensing methods to study historic LC changes in the basin were tried and evaluated against reference data collected during a field visit in Namibia in October 2005. Eventually, two methods were selected and applied to pre-processed Landsat MSS and ETM+ satellite image mosaics of 1973 and 2001 respectively: 1. a combined unsupervised classification and pattern-recognition change detection method providing quantified and geographically distributed binary LC class change trajectory information and, 2. an NDVI (Normalised Difference Vegetation Index) change detection method providing quantified and geographically distributed continuous information on degrees of change in vegetation vigour. In addition, available documents and people initiated in the basin conditions were consulted in the pursuit of discerning major societal trends that the basin had undergone during the Angolan civil war. Finally, concentrations of nutrients (total phosphorous & total nitrogen), bacteria (faecal coliforms & faecal streptococci), conductivity, total dissolved solids, dissolved oxygen, pH, temperature and Secchi depth were sampled at 11 locations upstream and downstream of large-scale agricultural facilities and an urban area during the aforementioned field visit.</p><p>The nature, extent and geographical distribution of LC changes in the study area during the Angolan civil war were determined. The study area (150 922 km<sup>2</sup>) was the Angolan and Namibian parts of the basin. The results indicate that the vegetation vigour is dynamic and has decreased overall in the area, perhaps connected with precipitation differences between the years. However while the vigour decreased in the northwest, it increased in the northeast, and on more local scales the pattern was often more complex. With respect to migration out of Angola into Namibia, the LC changes followed expectations of more intense use in Namibia close to the border (0-5 km), but not at some distance (10-20 km), particularly east of Rundu. With respect to urbanisation, expectations of increased human impact locally were observed in e.g. Rundu, Menongue and Cuito Cuanavale. Road deterioration was also observed with Angolan urbanisation but some infrastructures appeared less damaged by the war. Some villages (e.g. Savitangaiala de Môma) seem to have been abandoned during the war so that the vegetation could regenerate, which was expected. But other villages (e.g. Techipeio) have not undergone the same vegetation regeneration suggesting they were not abandoned. The areal extent of large-scale agriculture increased 59% (26 km<sup>2</sup>) during the war, perhaps as a consequence of population growth. But the expansion was not nearly at par with the population growth of the Kavango region (320%), suggesting that a smaller proportion of the population relied on the large-scale agriculture for their subsistence in 2001 compared with 1973.</p><p>No significant impacts were found from the large-scale agriculture and urbanisation on the water quality during the dry season of 2005. Total phosphorous concentrations (with range: 0.067-0.095 mg l<sup>-1</sup>) did vary significantly between locations (p=0.013) but locations upstream and downstream of large-scale agricultural facilities were not significantly different (p=0.5444). Neither did faecal coliforms (range: 23-63 counts per 100ml) nor faecal streptococci (range: 8-33 counts per 100ml) vary significantly between locations (p=0.332 and p=0.354 respectively). Thus the impact of Rundu and the extensive livestock farming along the border were not significant at this time. The Cuito river on the other hand significantly decreased both the conductivity (range: 27.2-49.7 μS cm<sup>-1</sup>, p<0.0001) and the total dissolved solid concentration (range: 12.7-23.4 mg l<sup>-1</sup>, p<0.0001) of the mainstream of the Okavango during the dry season.</p><p>Land cover changes during the Angolan civil war, contributing causes and effects on water quality were studied in this research effort. Many of the obtained results can be used directly or with further application as a knowledge base for sustainable decision-making and management in the basin. Wisely used by institutions charged with that objective, the information can contribute to sustainable development and the ending of suffering and poverty for the benefit of the peoples of the Okavango and beyond.</p>
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Minas, Michael Getachew. "Characterization of plant-water interaction in Kilombero River Catchment in Tanzania using Normalized Difference Vegetation Index (NDVI)." Thesis, Stockholms universitet, Institutionen för naturgeografi och kvartärgeologi (INK), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-110913.
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Remote-sensing based indices such as Normalized Difference Vegetation Index have yielded valuable information about plant health. As the availability of water is one of the factors that controls plant's response to their environment, it is possible to indirectly studythe hydrology of an area via vegetation indices. Hence the thesis work used this tool to characterize the potential shifts in vegetation cover within and between years in Kilombero river catchment in Tanzania and make connection to the hydrology in the area. Separate time series analyses conducted on data pertaining to NDVI values and the areal coverage variability of arbitrarily defined NDVI-classes. The former data was extracted from a naturally vegetated wetland in the middle of the catchment while the latter from the topographically defined areas of the catchment. Results from the analyses showed that bothdatasets are sensitive to the seasonal rainfall while at inter-annual scale the areal coverage variability displayed significant correlations with past precipitation. Meanwhile the relatively higher sensitivity of the lowland area‟s NDVI to precipitation conforms to the initial assumption which emphasizes the importance of the wetland sub-catchment codenamed 1KB17 in describing Kilombero‟s hydrology. But the datasets show weak trends and it was not possible to make accurate future predictions on the hydrological conditions in the area. Meteorological distortions like clouds and environmental processes such as climate patterns or disturbances might have caused the problem in trend detection. Further studies needed to shed more light on the connection between land cover and hydrologic response in Kilombero.
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Muche, Muluken Eyayu. "Surface water hydrologic modeling using remote sensing data for natural and disturbed lands." Diss., Kansas State University, 2016. http://hdl.handle.net/2097/32609.
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Doctor of Philosophy<br>Department of Biological & Agricultural Engineering<br>Stacy L. Hutchinson<br>The Soil Conservation Service-Curve Number (SCS-CN) method is widely used to estimate direct runoff from rainfall events; however, the method does not account for the dynamic rainfall-runoff relationship. This study used back-calculated curve numbers (CNs) and Normalized Difference Vegetation Index (NDVI) to develop NDVI-based CNs (CN[subscript]NDV) using four small northeastern Kansas grassland watersheds with average areas of 1 km² and twelve years (2001–2012) of daily precipitation and runoff data. Analysis indicated that the CN[subscript]NDVI model improved runoff predictions compared to the SCS-CN method. The CN[subscript]NDVI also showed greater variability in CNs, especially during growing season, thereby increasing the model’s ability to estimate relatively accurate runoff from rainfall events since most rainfall occurs during the growing season. The CN[subscript]NDVI model was applied to small, disturbed grassland watersheds to assess the model’s ability to detect land cover change impact for military maneuver damage and large, diverse land use/cover watersheds to assess the impact of scaling up the model. CN[subscript]NDVI application was assessed using a paired watershed study at Fort Riley, Kansas. Paired watersheds were identified through k-means and hierarchical-agglomerative clustering techniques. At the large watershed scale, Daymet precipitation was used to estimate runoff, which was compared to direct runoff extracted from stream flow at gauging points for Chapman (grassland dominated) and Upper Delaware (agriculture dominated) watersheds. In large, diverse watersheds, CN[subscript]NDVI performed better in moderate and overall flow years. Overall, CN[subscript]NDVI more accurately simulated runoff compared to SCS-CN results: The calibrated model increased by 0.91 for every unit increase in observed flow (r = 0.83), while standard CN-based flow increased by 0.506 for every unit increase in observed flow (r = 0.404). Therefore, CN[subscript]NDVI could help identify land use/cover changes and disturbances and spatiotemporal changes in runoff at various scales. CN[subscript]NDVI could also be used to accurately estimate runoff from precipitation events in order to instigate more timely land management decisions.
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Roberson, Travis Leon. "Improving Soil Moisture Assessment of Turfgrass Systems Utilizing Field Radiometry." Thesis, Virginia Tech, 2019. http://hdl.handle.net/10919/87391.
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The need for water conservation continues to increase as global freshwater resources dwindle. In response, many golf course superintendents are implementing new methods and tools to become more frugal with their water applications. For example, scheduling irrigation using time-domain reflectometer (TDR) soil moisture sensors can decrease water usage. Still, TDR measurements are time-consuming and only cover small scales, leading to many locations being unsampled. Remotely sensed data such as the normalized difference vegetation index (NDVI) offer the potential of estimating moisture stress across larger scales; however, NDVI measurements are influenced by numerous stressors beyond moisture availability, thus limiting its reliability for irrigation decisions. An alternative vegetation index, the water band index (WBI), is primarily influenced by water absorption within a narrow spectral range of near-infrared light. Previous research has established strong relationships between moisture stress of creeping bentgrass (CBG) grown on sand-based root zones, a typical scenario for golf course putting greens. However, this relationship characterizes only a small portion of total acreage across golf courses, which limits widespread adoption. In our research, �007� CBG and �Latitude 36� hybrid bermudagrass (HBG) were grown on three soil textures, USGA 90:10 sand (S), sand loam (SL) and clay (C), arranged in a 2 x 3 factorial design, randomized within six individual dry-down cycles serving as replications. Canopy reflectance and volumetric water content (VWC) data were collected hourly between 0700 and 1900 hr using a hyperspectral radiometer and an embedded soil moisture sensor, until complete turf necrosis. The WBI had the strongest relationship to VWC (r = 0.62) and visual estimations of wilt (r = -0.91) compared to the green-to-red ratio index (GRI) or NDVI. Parameters associated with non-linear regression were analyzed to compare grasses, soils, indices, and their interactions. The WBI and GRI compared favorably with each other and indicated significant moisture stress approximately 28 hr earlier than NDVI (P = 0.0010). WBI and GRI respectively predicted moisture stress 12 to 9 hr before visual estimation of 50% wilt, whereas NDVI provided 2 hr of prediction time (P = 0.0317). When considering the time to significant moisture stress, the HBG lasted 28 hr longer than CBG, while S lasted 42 hr longer than either SL and C (P �� 0.0011). Nonlinear regression analysis showed that WBI and GRI can be useful for predicting moisture stress of CBG and HBG grown on three diverse soils in a highly controlled environment. Our results provide substantial evidence and direction for future research investigating how WBI and GRI can expedite moisture stress assessment and prediction on a large-acreage basis.<br>Master of Science in Life Sciences<br>Managed turfgrasses provide several benefits including filtering pollutants, cooling their surroundings, generating oxygen, preventing erosion, serving as recreational surfaces, and increasing landscape aesthetics. Intensively managed turfgrass systems, such as on golf courses and sports fields, require more inputs to maintain acceptable conditions. Freshwater use is often excessive on intensively managed turfgrasses to maintain proper plant growth. Drought conditions often limit water availability, especially in regions with limited rainfall. Turf managers tend to over-apply water across large acreage when few localized areas begin to show symptoms of drought. Additionally, turf managers sometimes wrongly identify stressed areas from other factors as ones being moisture-deprived. Advancements such as the use of soil moisture meters have simplified irrigation decisions as an aid to visual inspections for drought stress. While this method enhances detection accuracy, it still provides no solution to increase efficiency. Expanding our current knowledge of turfgrass canopy light reflectance for rapid moisture stress identification can potentially save both time and water resources. The objective of this research was to enhance our ability to identify and predict moisture stress of creeping bentgrass (CBG) and hybrid bermudagrass (HBG) canopies integrated into varying soil textures (USGA 90:10 sand (S), sand loam (SL) and Clay (C)) using light reflectance measurements. Dry-down cycles were conducted under greenhouses conditions collecting soil moisture and light reflectance data every hour from 7 am to 7 pm after saturating and withholding water from established plugs. Moisture stress was most accurately estimated over time using two vegetation indices, the water band index (WBI) and green-to-red ratio index (GRI), with approximately ninety percent accuracy to visible wilt stress. The WBI and GRI predicted moisture stress of CBG in all soil types and HBG in SL and C approximately 14 hours before the grasses reached 50% wilt. While light reflectance varies on exposed soils, our research shows that underlying soils do not interfere with measurements across typical turfgrass stands. This research provides a foundation for future research implementing rapid, aerial measurements of moisture stressed turfgrasses on a broad application of CBG and HBG on constructed or native soils.
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Cuéllar, Ana Carolina. "Uso de sensores remotos para la predicción de casos de Malaria en el departamento Orán, Salta, Argentina." Master's thesis, 2014. http://hdl.handle.net/11086/2781.
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tesis (magister en aplicaciones espaciales de alerta y respuesta temprana a emergencias)--universidad nacional de córdoba, facultad de matemática, astronomía y física, 2014.<br>maestría conjunta con el instituto de altos estudios espaciales "mario gulich"-conae.<br>en la malaria, enfermedad parasitaria que afecta a millones de personas en el mundo, los mosquitos del género anopheles han sido incriminados en su transmisión, existiendo reportes para argentina, de conocidas especies vectores también en américa. el presente trabajo está enfocado al uso de sensores remotos para la predicción de casos de malaria en el extremo noroeste de argentina. el estudio se realiza en la ciudad de san ramón de la nueva orán, donde fueron reportados casos desde 1986 hasta 2005. se analiza la relación existente entre los casos de malaria reportados y las variables ambientales/climáticas, índice normalizado de vegetación (ndvi), índice normalizado de agua (ndwi) y temperatura de superficie (lst)) obtenidas de imágenes satelitales landsat 5 y 7, mediante análisis de regresión multinivel de poisson. se observó una fluctuación estacional de los casos de malaria, con una mayor cantidad de enfermos reportada para los meses de verano. se genera un modelo de series temporales arima, que incluye las variables ambientales, y puede pronosticar los casos de malaria ocurridos durante el año 2000. a su vez, la relación entre los casos de malaria y los factores ambientales/climáticos muestra mediante el uso de la razón de la tasa de incidencia (irr), que los casos de malaria están asociados a un aumento en la lst media como así también a una disminución del ndvi. se espera que este trabajo pueda ser utilizado como base para el desarrollo de futuras acciones de prevención y control por parte de las autoridades en salud.<br>malaria is a parasitic disease that affects millions of people in the world. mosquitoes of the anopheles genus have been incriminated in the transmission. in argentina, there are reports of vector species also known in america. this research was focused on the use of remote sensing for the prediction of malaria cases in the northwest of argentina. this study was carried out using reported cases of the disease from san ramón de la nueva orán city, from 1986 until 2005. the relation between malaria cases and environmental variables (such as normalized difference vegetation index (ndvi), normalized difference water index (ndwi) and land surface temperature (lst)) obtained from satellite imagery, using multilevel poisson regression analysis, were analyzed. it was noted a seasonal fluctuation in the number of cases of malaria, with a greater number of patients reported for summer months. an arima time series model, which included the environmental variables, was developed and it was possible to predict the number of cases of malaria occurred during the year 2000. at the same time, the relation between malaria cases and environmental factors showed through the use of reason in the incidence rate (irr), that malaria cases were associated with an increase in the lst as well as well as a decrease of the ndvi. this work can be used as the basis for future prevention and control actions by health authorities.
Book chapters on the topic "Normalised Difference Water Index (NDWI)"
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Koutsias, Nikos, Iliana Kalogeropoulou, Anastasia Karamitsou, Nikoletta G. Mili, and Magdalini Pleniou. "A rule-based semi-automatic method to map burned areas using Landsat and Sentinel-2 images – incorporating vegetation indices into the mapping algorithm." In Advances in Forest Fire Research 2022. Imprensa da Universidade de Coimbra, 2022. http://dx.doi.org/10.14195/978-989-26-2298-9_7.
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At local or regional scales, where Landsat has been extensively applied to monitor burned areas, semi- or fully-automated methods are not very common. Koutsias et al. (2013) developed and improved (2021) a semi-automatic method to map burned areas consisted of a set of rules that are valid especially when the post-fire image has been captured shortly after the fire. However, the rule-based approach is not free of errors that eventually create limitations to adopt this method for reconstructing the fire history in a fully automated mode. In this work, we improved the method by incorporating vegetation indices. The vegetation indices evaluated were the: (i) Normalized Difference Vegetation Index (NDVI), (ii) Ratio Vegetation Index (RVI), (iii) Normalized Burn Ratio (NBR), (iv) Normalized Difference Water Index (NDWI) and (v) Shortwave Infrared Water Stress Index (SIWSI).
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Wijitdechakul Jinmika, Kiyoki Yasushi, Sasaki Shiori, and Koopipat Chawan. "A Multispectral Imaging and Semantic Computing System for Agricultural Monitoring and Analysis." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2017. https://doi.org/10.3233/978-1-61499-720-7-314.
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Multispectral image becomes widely used for environmental analysis to detect an object or phenomena that human eyes cannot capture. One of the main type of images acquired by remote sensing such as satellite or aircraft for earth observation. This paper presents a multispectral analysis for aerial images that captured by dual cameras (visible and infrared camera), which are mounted on an unmanned autonomous vehicle (UAV) or Drone. In our experiments, four spectral bands (three visible and one infrared band) were imaged, processed and analyzed to detect agricultural area and measure the health of vegetation. To interpret environmental phenomena and realize an environmental analysis, this study applies semantic analysis by creating a multispectral semantic image space, combined with three numerical indicators (the normalized difference vegetation index (NDVI), the normalized difference water index (NDWI) and the soil adjusted vegetation index (SAVI)) that can be used to analyze plant health, photosynthetic activity and detect environmental object to determine an agricultural area. This paper also proposed the concept of multi-spectrum semantic-image space for agricultural monitoring by defining the correlation meaning from multi-dimensional parameters which related to agricultural analysis to realize and explain agriculture conditions. This paper presents the experimental study on a rice field, a cornfield, a salt farm and a coconut farm in Thailand.
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Aguirre, Ignacio, Javier Lozano-Parra, and Jacinto Garrido Velarde. "Reservoir Time-Series Filling From Remote Sensing Data in the Central Valley, Chile." In Analyzing Sustainability in Peripheral, Ultra-Peripheral, and Low-Density Regions. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-4548-8.ch007.
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Reservoirs play a fundamental role in the hydrological planning of the central valley of Chile as they provide water for human and animal consumption, energy generation, and crop irrigation, especially during the summer season. In agriculture, reservoirs represent a significant source to keep the food security standard for more than half of the population of the country. The water management plans need complete records of their volume to calculate rules of operation or future scenarios; however, currently, these time series include gaps that do not allow better analysis, which increases uncertainty. To address this, the authors test a methodology to assess Sentinel 2 imagery through normalized difference water index (NDWI). The results correctly represent the temporality and seasonality of reservoir dynamics; however, the magnitude of the changes is not well represented when the reservoir is delivering water. This research allows more data-based planning of water resources in the central zone, contributing to better decision-making and more efficient water management.
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Scarpini, Eloisa. "Water Quality Monitoring of Shallow Lakes Through Google Earth Engine." In ATHENA Research Book, Volume 2. University of Maribor, University Press, 2023. http://dx.doi.org/10.18690/um.4.2023.51.
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This work focuses on the monitoring of water quality parameters in Trasimeno lake, a shallow water body located in central Italy, in the period 2015- 2022. To this aim ad hoc Google Earth Engine (GEE) based routines are implemented to analyse data from Sentinel 2 multispectral images. The spatial distribution of turbidity and chlorophyll-a concentration can be derived through the spectral band ratio method: a semi-empirical approach expressed by the mathematical ratio between the reflectance of two or more spectral bands. Two normalized indices are considered: NDTI - Normalized Difference Turbidity Index and NDCI Normalized Difference Chlorophyll-a Index. The results show an increasing trend both for turbidity and Chlorophyll-a concentration over time, highlighting a light worsening in Trasimeno lake quality status.
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A.S. Galvanin, Edinéia, Natalia V. Revollo, Federico Javier Beron de la Puente, Veronica Gil, Sandra Mara Alves da Silva Neves, and Paula Zapperi. "Monitoring and mapping of the Brazilian Pantanal wetland." In Vegetation Index and Dynamics - Methodologies for Teaching Plant Diversity and Conservation Status [Working Title]. IntechOpen, 2023. http://dx.doi.org/10.5772/intechopen.1002484.
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The Pantanal is one of the largest wetlands in the world. This natural region has fundamental importance for water supply and biodiversity conservation. In this paper, we apply a methodology to analyze multi-temporal water and vegetation changes in six different zones of land use/land cover and their relationship with humidity in the Brazilian Pantanal subregion of Cáceres, Mato Grosso. Meteorological data from the INMET Station in Cáceres was used. The Normalized Difference Vegetation Index (NDVI) and Normalized Difference Moisture Index (NDMI) were compared year to year (2019, 2020, 2021). The spatial variations revealed water changes according to the occurrence of wet and dry years. In dry years there was a remarkable increase in mean and maximum values linearly related to a decrease in water availability. Analyzing LULC change dynamics in these areas is crucial for developing new proposals for interventions to monitor the area and thus provide subsidies to goal 15 of the 2030 Sustainable Development Agenda, which aims to stop and restore degradation caused in the environment and to promote reforestation.
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Victor Chukwuka, Azubuike, and Ozekeke Ogbeide. "Riparian-Buffer Loss and Pesticide Incidence in Freshwater Matrices of Ikpoba River (Nigeria): Policy Recommendations for the Protection of Tropical River Basins." In River Basin Management - Sustainability Issues and Planning Strategies. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.95521.
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The unregulated use of watersheds for agriculture negatively impacts the quality of river basins. In particular, the reduced quality of surface-waters, have been attributed to absence or poorly-decided riparian-buffer specifications in environmental laws. To demonstrate suitable buffer-width for protection of surface water, sediment and benthic fish populations, five riparian areas with different vegetation richness and buffer-width were selected within an organochlorine pesticide (OCP)-impacted watershed using the Normalized Differential Vegetation Index (NDVI) and multiple buffer analysis respectively. Mean OCP levels in surface water, sediment and fish sampled at each riparian stations showed site-specific differences with markedly higher levels of α-BHC, β-BHC, δ-BHC, p,p′-DDD and total pesticide residues at stations with least riparian cover. The principal component analysis further revealed more OCPs associating with sediment and fish from stations having smaller buffer-width and sparse riparian vegetation. Stations with wider buffer-width of at least 120 m provided greater protection to adjacent surface water and benthic fish populations. While this study recommends riparian buffer-widths for a typical tropical environment, further research which assesses other contaminant types in aquatic matrices adjacent to different riparian environments would be valuable and informative for regulatory guidance and strategic protection of ecosystem services.
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Jermthaisong Panath, Kingpaiboon Sununtha, and Chawakitchareon Petchporn. "Estimating Actual Evapotranspiration from NDVI Using Landsat 8 and Sentinel-2 Imagery." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2018. https://doi.org/10.3233/978-1-61499-834-1-164.
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Evapotranspiration (ET) is the sum of evaporation and transpiration from the surface to the atmosphere. ET is a hydrologic cycle component and importance in agricultural water management such as schedules of irrigation water requirement, irrigation system design and watershed management. Evapotranspiration can be measured or estimated using several methods. The objectives of this study were simple methods and quick analyses for the estimating of crop coefficients (Kc) and daily actual evapotranspiration (ETa) from Normalized Difference Vegetation Index (NDVI) derived from Landsat 8 and Sentinel-2 satellite images. Subsequent comparison of ETafrom both satellite images at Ban Nong Bua, Amphoe Ban Fang, Khon Kaen Province, Thailand. The results showed that good correlation between NDVI and ETafrom both satellite images (coefficient of determination (R2) = 0.85 and 0.87, respectively). Both satellite images can be used or combined to detect temporal and spatial analysis. Sentinel-2 can provide more fine resolution of 10 m in VIS-NIR bands than Landsat 8 and is suitable for precision farming or site specific management, while Landsat 8 TIRS bands were advantages.
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Gad, Abd-alla. "Remotely Sensed Data for Assessment of Land Degradation Aspects, Emphases on Egyptian Case Studies." In Sustainable Energy Investment - Technical, Market and Policy Innovations to Address Risk. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.90999.
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Remote sensing and thematic data were used to provide comprehensive views of surface conditions related to land degradation and desertification, considered environmental extremes in arid and semi-arid regions. The current work applies techniques, starting with simple visual analyses up to a parametric methodology, adopted from the FAO/UNEP and UNESCO provisional methodology for assessment and mapping of soil degradation. Egyptian case studies are highlighted to insinuate on studied aspects. Variable satellite imageries (MSS, TM, and ETM) and aerial photographs were utilized to provide data on soil conditions, land cover, and land use. IDRISI and ArcGIS software were used to manage thematic data, while ERDAS IMAGIN was used to process satellite data and to derive the normalized difference vegetation index (NDVI) values. A GIS model was established to modify the universal soil loss equation (USLE) calculating the present state and risk of soil degradation. The study area is found exposed to slight hazard of water erosion, however, and to high risk of wind erosion. It is also threatened by a slight to high salinization and slight to moderate physical degradation. It is recommended to use a GIS in detailed and very detailed studies for evaluating soil potentiality in agricultural expansion areas.
Conference papers on the topic "Normalised Difference Water Index (NDWI)"
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Liu, Yuyu, Hao Wang, Ce Han, and Yongfei Fu. "Analysis of Normalized Different Wetness Index (NDWI) Using Landsat Imagery for Confluent Area of Multi Water Resources in Western Jinan." In 2024 6th International Conference on Communications, Information System and Computer Engineering (CISCE). IEEE, 2024. http://dx.doi.org/10.1109/cisce62493.2024.10653415.
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Cardia, Luiz Henrique, André Luis Sotero Salustiano Martin, and Laura Maria Canno Ferreira Fais. "Uso da ferramenta ndwi para modelo de análise de disponibilidade de água." In INTERNATIONAL WORKSHOP FOR INNOVATION IN SAFE DRINKING WATER. Universidade Estadual de Campinas, 2022. http://dx.doi.org/10.20396/iwisdw.n1.2022.4804.
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Environmental problems related to water resources and soil degradation are increasingly frequent, resulting in changes in the hydrological cycle and in the fluvial characteristics of hydrographic basins. The presence of small dams along the hydrographic network can contribute to the guarantee of ecological flow, even in periods of drought. However, the identification and location of these structures is difficult, as they often do not have the proper authorization. Thus, this work aims to evaluate the availability of water in the Piraí River Basin, using the Normalized Difference Water Index (NDWI) tool, based on satellite images. The Ribeirão Piraí sub-basin is located in the region of the Piracicaba-Capivari-Jundiaí River Basin (PCJ), and has accelerated urban growth, which together with the increase in other human activities in recent decades, contributes to a significant change in several natural cycles. From the results, it is observed that the tool proved to be adequate to perform the temporal analysis of the formation of water mirrors (through the sequential analysis of successive satellite images) to understand the regional dynamics, as well as the general water availability of the basin.
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Madhurshan, R., K. I. K. Dhananjaya, T. Keerthi, S. M. Dassanayake, and Z. Chao. "Enhancing operational strategies in water reservoir management through satellite imagery: analysing temporal anomalies in water surface variations for climate adaptation under seasonal changes." In International Conference on Business Research. Business Research Unit (BRU), 2024. https://doi.org/10.31705/icbr.2024.19.
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Climate change variations have significant adverse impacts on water resources, particularly in regions where the water supply is primarily dependent on reservoir systems. For efficient management of water resources, it is essential to comprehend the dynamics of reservoir water levels and climate-driven anomalies. Quantitatively appraising the water budget is crucial for enhancing socio-economic water and energy demands. Analyzing fluctuations in water levels and cyclic patterns of drought seasons due to climate change can significantly aid in the pre-planning and managing reservoir systems. This study aims to enhance water reservoir management by using satellite imagery to identify drought periods through surface water area analysis. With the fusion of Landsat 8 and Sentinel-1 data, this research focuses on mapping water surface changes at the Victoria Lake reservoir, Sri Lanka, using the Normalized Difference Water Index (NDWI) from 2018 to 2023. Both satellite data were acquired and subsequently processed on the Google Earth Engine platform (GEE). The resulting maps were created using ArcMap desktop software. The correlation coefficient observed between Landsat 8 and Sentinel-1 NDWI area measurements is 0.771, indicating a strong relationship between the two datasets. This high correlation underscores the reliability of using both sources to comprehensively analyze water surface area. Factors such as sensor calibration, atmospheric conditions, and data processing techniques can affect recorded values and correlations. Results revealed a cyclic pattern in water levels, with a notable trough in March 2019, followed by a significant drop lasting until March 2022, and another rapid decline observed within the subsequent year. Integrating satellite imagery in monitoring and decision-making processes offers a valuable tool for addressing the challenges of water and energy management under climate anomalies.
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Rawat, Kishan Singh, Smruti Ranjan Sahu, Sudhir Kumar Singh, Sharad Chander, and Ashwin Gujrati. "Water Quality Analysis Using Normalized Difference Chlorophyll Index (NDCI) and Normalized Difference Turbidity Index (NDTI), Using Google Earth Engine Platform." In 2023 International Conference on Modeling, Simulation & Intelligent Computing (MoSICom). IEEE, 2023. http://dx.doi.org/10.1109/mosicom59118.2023.10458842.
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Dudumashe, N., and A. Thomas. "ASSESSMENT OF LAND SURFACE TEMPERATURE AND DROUGHT INDICES FOR THE KLERKSDORP-ORKNEY-STILFONTEIN-HARTEBEESFONTEIN (KOSH) REGION." In Лесные экосистемы в условиях изменения климата: биологическая продуктивность и дистанционный мониторинг. Crossref, 2020. http://dx.doi.org/10.25686/7230.2020.6.58828.
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Land surface temperature (LST) is a key calculator of local climate, vegetation growth, and urban change. Spatial and temporal variation of LST over land use/land cover (LULC) features results in changes in environmental factors that influence the characteristics of the land surface. In this study, some remote sensing techniques have been applied to Landsat 8 data acquired during summer and spring seasons of years 2019, 2018, and 2013 to estimate normalized difference vegetation index (NDVI), LST, normalized difference built-up index (NDBI), three drought indices viz. vegetation supply water index (VWSI), crop water supply index (CWSI) and temperature condition index (TCI) and analysed the spatial-temporal trends in LST among major LULC of an arid part of North West Province of South Africa known as Klerksdorp-Orkney-Stilfontein-Hartebeesfontein (KOSH) region. The results shows that there is a direct negative relationship between LST and NDVI. The study compared three drought indices for monitoring the water scarcity of the area. The finding also indicates a positive relationship between LST and CWSI that is relevant to soil moisture and NDBI. The findings from the study prove the capability of optical remote sensing in monitoring LST and drought in the region. The study reveals the usefulness of the remotely sensed data of Landsat 8 satellite in estimating LST and drought changes in the KOSH area. This study also tried to assess the usefulness of Landsat 8 bands in deriving vegetation index and drought indices for monitoring drought in the KOSH region during three years (2013, 2018 and 2019).
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Zhao, Tiebiao, YangQuan Chen, Andrew Ray, and David Doll. "Quantifying Almond Water Stress Using Unmanned Aerial Vehicles (UAVs): Correlation of Stem Water Potential and Higher Order Moments of Non-Normalized Canopy Distribution." In ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/detc2017-68246.
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Optimization of water use relies on accurate measurement of water status of crops. Stem water potential (SWP) has become one of the most popular methods to monitor the water status of almond trees. However, it needs to take twice visit and at least thirty minutes to obtain one measurement, which makes it very difficult to understand the water status information in the orchard level. Unmanned aerial vehicle (UAV) based remote sensing promises to deliver reliable and precise field-scale information more efficiently by providing multispectral higher-resolution images with much lower cost and higher flexibility. This paper aims to extract almond water status from UAV-based multispectral images via building the correlation between SWP and vegetation indices. Different from the traditional method that focuses on normalized difference vegetation index (NDVI) means, higher-order moments of non-normalized canopy distribution descriptors were discussed to model SWP measurements. Results showed that the proposed methods performed better than traditional NDVI mean.
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Kandegedara, N. C., S. M. Dassanayake, and I. Mahakalanda. "Impacts of urban impervious surface expansion on rice fields in north central province, Sri Lanka: a GIS-based temporal analysis." In International Conference on Business Research. Business Research Unit (BRU), 2024. https://doi.org/10.31705/icbr.2024.25.
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Urbanization drives the increase of impervious surfaces, thereby significantly diminishing the rice field areas over time. This study examines the impact of urbanization on rice fields in Sri Lanka's North Central Province, particularly in the Anuradhapura and Polonnaruwa districts. Using quarterly Sentinel-2 satellite imagery and a pixel-based classification approach, the analysis incorporates remote sensing indices such as the Modified Normalized Difference Water Index (MNDWI), Enhanced Vegetation Index (EVI), and Normalized Difference Built-up Index (NDBI) to map and quantify the expansion of impervious surfaces. Conducted on the Google Earth Engine (GEE) platform, this research highlights a significant reduction in rice field areas between 2019 and the fourth quarter of 2023, directly correlated with the growth of impervious urban surfaces. The findings underscore the urgent need for effective land management strategies to mitigate the adverse effects of urbanization on agricultural land use. This study offers critical insights and recommendations for sustainable urban planning, with implications for food security, economic stability, and ecosystem health in the region.
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Vercruysse, Joachim, and Greet Deruyter. "OPTIMISING VEGETATION-INPUT FOR DROUGHT ASSESSMENT WITH SENTINEL-2A DATA." In 22nd SGEM International Multidisciplinary Scientific GeoConference 2022. STEF92 Technology, 2022. http://dx.doi.org/10.5593/sgem2022/2.1/s10.40.
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As a consequence of climate change, in some regions, more intense rain showers go hand in hand with longer dry periods. The subsequent more and more severe droughts can have devastating effects on many economic and social sectors. Therefore, it is necessary to be able to predict and assess the consequences of these droughts on a local scale, in order to develop policies to cope. Drought assessment needs a lot of detailed and accurate input-data, such as land use, land cover, soil moisture, vegetation, evapotranspiration, etc., often obtained by continuous earth monitoring by satellites. Satellite images are generally converted into indices, of which the Normalized Difference Vegetation Index (NDVI) is one of the most widely used. It was developed for use with Landsat imagery and allows for the classification of satellite images for land use and the assessment of the vegetation�s vitality. In this research, a new composite index is presented and compared to the NDVI to be used with Sentinel-2A imagery, having higher resolution and more spectral bands than Landsat. This new composite index can be used to detect water and vegetation. Test results show that this newly developed composite index achieves a better accuracy through Support Vector Machine (SVM) classification than the widely used NDVI. Although further validation is necessary, the results promise a possible amelioration of vegetation related input data for drought assessment and management.
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GHERASIM, Paul Marian, Mihai DIMA, and Ioana AGAPIE (MEREUȚĂ). ""Studing LST and NDVI Values for Suhi Non-Suhi Occupied by Constructions and Buildings: a Case Study of Iasi. "." In Air and Water – Components of the Environment 2022 Conference Proceedings. Casa Cărţii de Ştiinţă, 2022. http://dx.doi.org/10.24193/awc2022_11.
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In this paper we tried to study the values of radiant temperatures (Land Surface Temperature) and NDVI (Normalized Difference Vegetation Index) for areas occupied by buildings and green spaces. The area affected by the Urban Heat Island (UHI) was also determined. Study Area, Iasi, the largest city in eastern Romania, is geographically situated on latitude 47°12'N to 47°06'N and longitude 27°32'E to 27°40'E. LST is an estimate of ground temperature and is important to identify change in environment. An important parameter in global climate change is rapid urbanization which leads to an increase in Land Surface Temperature (LST). The urban heat island (UHI) represents the phenomenon of higher atmospheric and surface temperatures occurring in urban area or metropolitan area than in the surrounding rural zones due to urbanization. It also been found that night UHI is more powerful than day. At night the LST values for SUHI varies between 24.5°C-25.9°C, and during the day between 35°C-38.7°C. With the development of remote sensing technology, it has become an important approach to urban heat island research. MODIS and Landsat data were used to estimate the LST and NDVI. From the analysis of the images it can be seen that the temperatures in SUHI are lower where there are green spaces around the buildings, and temperatures are higher in the non-UHI area, where inside or around the green spaces there are surfaces built or covered with concrete. Statistical data show very average temperatures for areas affected by UHI, 37.8°C for daytime and 24.6°C for night.
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MAGYARI-SÁSKA, Zsolt, and Ștefan DOMBAY. ""Experimental Method to Assess the Looseness or Compactness in Climate Changing for Several Major Cities of Hungary."." In Air and Water – Components of the Environment 2022 Conference Proceedings. Casa Cărţii de Ştiinţă, 2022. http://dx.doi.org/10.24193/awc2022_12.
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In this paper we tried to study the values of radiant temperatures (Land Surface Temperature) and NDVI (Normalized Difference Vegetation Index) for areas occupied by buildings and green spaces. The area affected by the Urban Heat Island (UHI) was also determined. Study Area, Iasi, the largest city in eastern Romania, is geographically situated on latitude 47°12'N to 47°06'N and longitude 27°32'E to 27°40'E. LST is an estimate of ground temperature and is important to identify change in environment. An important parameter in global climate change is rapid urbanization which leads to an increase in Land Surface Temperature (LST). The urban heat island (UHI) represents the phenomenon of higher atmospheric and surface temperatures occurring in urban area or metropolitan area than in the surrounding rural zones due to urbanization. It also been found that night UHI is more powerful than day. At night the LST values for SUHI varies between 24.5°C-25.9°C, and during the day between 35°C-38.7°C. With the development of remote sensing technology, it has become an important approach to urban heat island research. MODIS and Landsat data were used to estimate the LST and NDVI. From the analysis of the images it can be seen that the temperatures in SUHI are lower where there are green spaces around the buildings, and temperatures are higher in the non-UHI area, where inside or around the green spaces there are surfaces built or covered with concrete. Statistical data show very average temperatures for areas affected by UHI, 37.8°C for daytime and 24.6°C for night.
Reports on the topic "Normalised Difference Water Index (NDWI)"
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Johansen, Richard A., Christina L. Saltus, Molly K. Reif, and Kaytee L. Pokrzywinski. A Review of Empirical Algorithms for the Detection and Quantification of Harmful Algal Blooms Using Satellite-Borne Remote Sensing. U.S. Army Engineer Research and Development Center, 2022. http://dx.doi.org/10.21079/11681/44523.
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Harmful Algal Blooms (HABs) continue to be a global concern, especially since predicting bloom events including the intensity, extent, and geographic location, remain difficult. However, remote sensing platforms are useful tools for monitoring HABs across space and time. The main objective of this review was to explore the scientific literature to develop a near-comprehensive list of spectrally derived empirical algorithms for satellite imagers commonly utilized for the detection and quantification HABs and water quality indicators. This review identified the 29 WorldView-2 MSI algorithms, 25 Sentinel-2 MSI algorithms, 32 Landsat-8 OLI algorithms, 9 MODIS algorithms, and 64 MERIS/Sentinel-3 OLCI algorithms. This review also revealed most empirical-based algorithms fell into one of the following general formulas: two-band difference algorithm (2BDA), three-band difference algorithm (3BDA), normalized-difference chlorophyll index (NDCI), or the cyanobacterial index (CI). New empirical algorithm development appears to be constrained, at least in part, due to the limited number of HAB-associated spectral features detectable in currently operational imagers. However, these algorithms provide a foundation for future algorithm development as new sensors, technologies, and platforms emerge.
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Broussard, Whitney, Glenn Suir, and Jenneke Visser. Unmanned Aircraft Systems (UAS) and satellite imagery collections in a coastal intermediate marsh to determine the land-water interface, vegetation types, and Normalized Difference Vegetation Index (NDVI) values. Engineer Research and Development Center (U.S.), 2018. http://dx.doi.org/10.21079/11681/29517.
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Gavin, Greg, Paul Inkenbrandt, Trevor Schlossnagle, and Rebecca Molinari. Groundwater of Pahvant Valley, Millard County, Utah. Utah Geological Survey, 2024. http://dx.doi.org/10.34191/ss-173.
Full textAbstract:
Pahvant Valley, located in Millard County, Utah, encompasses 1610 square miles and includes several small towns, agricultural districts, hot springs, and biologically important wetlands, all heavily reliant on groundwater. This study, conducted by the Utah Geological Survey during 2022 and 2023, aims to define Pahvant Valley’s water recharge and discharge estimates, characterize its primary hydrogeologic units, and describe groundwater recharge and discharge areas. The research includes the collection of groundwater and surface water samples to estimate flow paths, sources of recharge and discharge, and residence times. Additionally, a water-level campaign was conducted in early March 2022 to create an updated potentiometric surface map for the region. Pahvant Valley’s groundwater system comprises three main aquifers: the valley-fill aquifer, the volcanic aquifer of the Tabernacle Hill and Ice Springs lava flows, and the Black Rock Desert volcanics. For this study, we delineated these aquifers into three conceptual groundwater zones based on hydrogeologic, geochemical, and potentiometric characteristics. Results of this study indicate significant groundwater level declines, particularly in agricultural areas, driven by overextraction and reduced recharge. Groundwater levels have declined by an average of 26 feet since 1986 and some areas have experienced declines of up to 160 feet. The study emphasizes the crucial role of streamflow from the Pahvant Range in recharging the valley-fill aquifer, with stable isotope and chemical analyses confirming that stream discharge significantly contributes to groundwater recharge. Additionally, groundwater quality varies across the valley and increased total dissolved solids could affect water usability in some areas. The analysis of irrigation practices reveals a significant shift in the early 1990s from flood irrigation to pivot irrigation, which led to increased and more consistent crop density and health, as indicated by Normalized Difference Vegetation Index (NDVI) data. From 1992 to 2021, NDVI values rose substantially in both magnitude and duration, reflecting higher crop yields over time. This increase in crop density and vitality resulted in higher evapotranspiration per acre, while the extended duration of greenness demonstrated stable yields regardless of surface water availability. As irrigation shifted from surface water to groundwater sources, numerous high-capacity wells were established to meet demand. Consequently, groundwater pumping in areas no longer reliant on surface water led to declines in groundwater elevations. These decreases in groundwater levels have been linked to land subsidence, with Interferometric Synthetic Aperture Radar (InSAR) analysis detecting up to 5 inches of ground deformation in the Meadow area between 2014 and 2022, closely associated with changes in groundwater levels. This study underscores the critical balance between groundwater extraction and recharge, the effects of irrigation practices on water use, and the importance of continuous monitoring and management to ensure sustainable groundwater resources. The findings highlight the need for sustainable groundwater management practices to maintain agricultural productivity and ecological health in Pahvant Valley.