Relevant bibliographies by topics / Normalized Difference Water Index

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Academic literature on the topic 'Normalized Difference Water Index'

Author: Grafiati
Published: 4 June 2021
Last updated: 6 February 2022

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Journal articles on the topic "Normalized Difference Water Index"

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Estallo, Elizabet Lilia, Francisco Felipe Ludueña-Almeida, Andrés Mario Visintin, Carlos Marcelo Scavuzzo, Mario Alberto Lamfri, María Virginia Introini, Mario Zaidenberg, and Walter Ricardo Almirón. "Effectiveness of normalized difference water index in modellingAedes aegyptihouse index." International Journal of Remote Sensing 33, no. 13 (December 22, 2011): 4254–65. http://dx.doi.org/10.1080/01431161.2011.640962.

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Rad, Arash Modaresi, Jason Kreitler, and Mojtaba Sadegh. "Augmented Normalized Difference Water Index for improved surface water monitoring." Environmental Modelling & Software 140 (June 2021): 105030. http://dx.doi.org/10.1016/j.envsoft.2021.105030.

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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 (July 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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Ji, Lei, Li Zhang, and Bruce Wylie. "Analysis of Dynamic Thresholds for the Normalized Difference Water Index." Photogrammetric Engineering & Remote Sensing 75, no. 11 (November 1, 2009): 1307–17. http://dx.doi.org/10.14358/pers.75.11.1307.

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Guo, Qiandong, Ruiliang Pu, Jialin Li, and Jun Cheng. "A weighted normalized difference water index for water extraction using Landsat imagery." International Journal of Remote Sensing 38, no. 19 (June 16, 2017): 5430–45. http://dx.doi.org/10.1080/01431161.2017.1341667.

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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 (April 30, 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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Al-Quraishi, Ayad M. F., Heman A. Gaznayee, and Mattia Crespi. "Drought trend analysis in a semi-arid area of Iraq based on Normalized Difference Vegetation Index, Normalized Difference Water Index and Standardized Precipitation Index." Journal of Arid Land 13, no. 4 (April 2021): 413–30. http://dx.doi.org/10.1007/s40333-021-0062-9.

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Mazhar, Nausheen, Dania Amjad, Kanwal Javid, Rumana Siddiqui, Muhammad Ameer Nawaz, and Zaynah Sohail Butt. "Mapping Fluctuations of Hispar Glacier, Karakoram, using Normalized Difference Snow Index (NDSI) and Normalized Difference Principal Component Snow Index (NDSPCSI)." International Journal of Economic and Environmental Geology 11, no. 4 (March 11, 2021): 48–55. http://dx.doi.org/10.46660/ijeeg.vol11.iss4.2020.516.

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Investigation of the fluctuations in the snow-covered area of the major glaciers of the Karakoram range is essential for proper water resource management in Pakistan, since its glaciers are responding differently to the rising temperatures. The objective of this paper is to map snow covered area of Hispar glacier in Hunza river basin for the years 1990, 2010 and 2018. Two techniques, (NDPCSI) Normalized Difference Principal Component Snow Index and (NDSI) Normalized Difference Snow Index were used. Hispar glacier of the Hunza basin has lost 114 km2 of its ice cover area, during the last 28 years, with an alarming annual retreat rate of 1.67 km2 of glacier ice from 1990 to 2018. Hunza basin experienced a +1°C rise in both mean minimum and mean maximum temperature during 2007 to 2018.as a result, Karakorum ice reserves have been affected by rising temperature of the region. Due to temperature rise, retreat of snowcovered area of Hispar, Karakoram mountain range shows a shift in the cryospheric hazard zone.
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Dennison, P. E., D. A. Roberts, S. H. Peterson, and J. Rechel. "Use of Normalized Difference Water Index for monitoring live fuel moisture." International Journal of Remote Sensing 26, no. 5 (March 2005): 1035–42. http://dx.doi.org/10.1080/0143116042000273998.

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Imran, Areeba Binte, Samia Ahmed, Waqar Ahmed, Muhammad Zia-ur-Rehman, Arif Iqbal, Naveed Ahmad, and Irfan Ullah. "Integration of Sentinel-2 Derived Spectral Indices and In-situ Forest Inventory to Predict Forest Biomass." Pakistan Journal of Scientific & Industrial Research Series A: Physical Sciences 64, no. 2 (July 5, 2021): 119–30. http://dx.doi.org/10.52763/pjsir.phys.sci.64.2.2021.119.130.

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Forest biomass estimation is the central part of sustainable forest management to assess carbon stocks and carbon emissions from forest ecosystem. Sentinel-2 is state-of-art sensor with refined spatial and recurrent temporal resolution data. The present study explored the potential of Sentinel-2 derived vegetation indices for above ground biomass prediction using four regression models (linear, exponential, power and logarithmic). Sentinel-2 indices includes Global environmental monitoring index, transformed normalized difference vegetation index, normalized difference water index, normalized difference infrared index and red-edge normalized difference vegetation index. The performances of Sentinel-2 indices were assessed by simple single variable (index) based regression for GEMI, TNDVI, NDII, NDWI and RENDVI versus AGB values. Further, stepwise linear regression was also developed in which used all indices entered into stepwise selection and the best index was selected in the final model. Results showed that linear model of all indices performance best compared to the rest three models and R2 values 0.12, 0.39, 0.46, 0.44 and 0.37 for Global environmental monitoring index, transformed normalized. Vegetation index, normalized difference water index, infrared index and red-edge vegetation index, respectively. Normalized difference water index was considered the best index among five computed indices in simple linear as well as in stepwise linear regression, whereas rest of the indices were removed because they were not significant under the stepwise criteria. Further, the accuracy of normalized difference water index model was determined by root mean square error and final prediction model has 28.27 t/ha error for both simple linear and stepwise linear regression. Therefore, normalized difference water index was selected for biomass mapping and resultant biomass showed up to 339 t/ha in the study area. The resultant biomass map also showed consistency with global datasets which include global forest canopy height and global forest tree cover change maps. The study suggest that Sentinel-2 product has great potential to estimate above ground biomass with accuracy and can be used for large scale mapping in combination with national forest inventory for carbon emission accounting.
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Dissertations / Theses on the topic "Normalized Difference Water Index"

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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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Neff, Kirstin Lynn. "Seasonality of Groundwater Recharge in the Basin and Range Province, Western North America." Diss., The University of Arizona, 2015. http://hdl.handle.net/10150/556969.

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Alluvial groundwater systems are an important source of water for communities and biodiverse riparian corridors throughout the arid and semi-arid Basin and Range Geological Province of western North America. These aquifers and their attendant desert streams have been depleted to support a growing population, while projected climate change could lead to more extreme episodes of drought and precipitation in the future. The only source of replenishment to these aquifers is recharge. This dissertation builds upon previous work to characterize and quantify recharge in arid and semi-arid basins by characterizing the intra-annual seasonality of recharge across the Basin and Range Province, and considering how climate change might impact recharge seasonality and volume, as well as fragile riparian corridors that depend on these hydrologic processes. First, the seasonality of recharge in a basin in the sparsely-studied southern extent of the Basin and Range Province is determined using stable water isotopes of seasonal precipitation and groundwater, and geochemical signatures of groundwater and surface water. In northwestern Mexico in the southern reaches of the Basin and Range, recharge is dominated by winter precipitation (69% ± 42%) and occurs primarily in the uplands. Second, isotopically-based estimates of seasonal recharge fractions in basins across the region are compared to identify patterns in recharge seasonality, and used to evaluate a simple water budget-based model for estimating recharge seasonality, the normalized seasonal wetness index (NSWI). Winter precipitation makes up the majority of annual recharge throughout the region, and North American Monsoon (NAM) precipitation has a disproportionately weak impact on recharge. The NSWI does well in estimating recharge seasonality for basins in the northern Basin and Range, but less so in basins that experience NAM precipitation. Third, the seasonal variation in riparian and non-riparian vegetation greenness, represented by the normalized difference vegetation index (NDVI), is characterized in several of the study basins and climatic and hydrologic controls are identified. Temperature was the most significant driver of vegetation greenness, but precipitation and recharge seasonality played a significant role in some basins at some elevations. Major contributions of this work include a better understanding of recharge in a monsoon-dominated basin, the characterization of recharge seasonality at a regional scale, evaluation of an estimation method for recharge seasonality, and an interpretation of the interaction of seasonal hydrologic processes, vegetation dynamics, and climate change.
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Guo, Qi. "Bangladesh Shoreline Changes During the Last Four Decades Using Satellite Remote Sensing Data." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1503258115717912.

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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
Department of Biological & Agricultural Engineering
Stacy L. Hutchinson
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.
Master of Science in Life Sciences
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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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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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.

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.

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.

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 km2) 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 km2) 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.

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-1) 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-1, p<0.0001) and the total dissolved solid concentration (range: 12.7-23.4 mg l-1, p<0.0001) of the mainstream of the Okavango during the dry season.

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.

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Pinheiro, Ana T. (Ana Torres) 1972. "Relationship between satellite-derived Normalized Difference Vegetation Index (NDVI) and surface hydrology." Thesis, Massachusetts Institute of Technology, 1998. http://hdl.handle.net/1721.1/46149.

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Griffin, Alicia Marie Rutledge. "Using LiDAR and normalized difference vegetation index to remotely determine LAI and percent canopy cover at varying scales." [College Station, Tex. : Texas A&M University, 2006. http://hdl.handle.net/1969.1/ETD-TAMU-1117.

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Abbasova, Tahira. "Detection and analysis of changes in desertification in the Caspian Sea Region." Thesis, Stockholms universitet, Institutionen för naturgeografi och kvartärgeologi (INK), 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-43241.

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The Caspian Region includes the Caspian Sea and five littoral states: Azerbaijan, Iran, Turkmenistan, Kazakhstan and Russian. 40% of the Caspian coastal zone is arid, 69% of this territory undergone desertification according to international reports. Among the reasons are soil erosion caused by water, wind and irrigation, the salinization of soil, intense bioresources usage, and soil pollution due to oil extraction and production. Desertification is a serious problem, at global, national and local scales. It is important to know what should be sustained or developed in order to protect land from desertification. The generalization of data over desertification processes in Caspian countries, studying the dynamics of this process in space and time could help facilitate measures to counter regional desertification. To understand Caspian Region coastal desertification phenomenon, vegetation cover satellite images for the years 1982 – 2006 were investigated to give map vegetation changes over time. The Normalized Difference Vegetation Index (NDVI) data for this study was derived from the Global Inventory Modeling and Mapping Studies (GIMMS) dataset, with the spatial resolution of 8 km. A coastal strip 160 km from the coast, divided by countries, was investigated. Theanalyses were focused on extent and severity of vegetation cover degradation, and possible causes such as landscape, land use history and culture, climatic changes and policies. The aim was to address questions related to desertification phenomenon, by focusing on Caspian Region time-series of vegetation cover data and investigation patterns of desertification in the region. In this study evidence of land degradation in the Caspian Region countries was found to occur on local scales or sub-national scales rather than across the regional as a whole. Changes in vegetation cover revealed by AVHRR NDVI appeared to be reversible in character and were dependent on the climate conditions, and anthropogenic impact in approximately equal proportions.
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Osunmadewa, Babatunde A., Christine Wessollek, and Pierre Karrasch. "Linear and segmented linear trend detection for vegetation cover using GIMMS normalized difference vegetation index data in semiarid regions of Nigeria." SPIE, 2015. https://tud.qucosa.de/id/qucosa%3A35266.

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Quantitative analysis of trends in vegetation cover, especially in Kogi state, Nigeria, where agriculture plays a major role in the region’s economy, is very important for detecting long-term changes in the phenological behavior of vegetation over time. This study employs the use of normalized difference vegetation index (NDVI) [global inventory modeling and mapping studies 3g (GIMMS)] data from 1983 to 2011 with detailed methodological and statistical approach for analyzing trends within the NDVI time series for four selected locations in Kogi state. Based on the results of a comprehensive study of seasonalities in the time series, the original signals are decomposed. Different linear regression models are applied and compared. In order to detect structural changes over time a detailed breakpoint analysis is performed. The quality of linear modeling is evaluated by means of statistical analyses of the residuals. Standard deviations of the regressions are between 0.015 and 0.021 with R2 of 0.22–0.64. Segmented linear regression modeling is performed for improvement and a decreasing standard deviation of 33%–40% (0.01–0.013) and R2 up to 0.82 are obtained. The approach used in this study demonstrates the added value of long-term time series analyses of vegetation cover for the assessment of agricultural and rural development in the Guinea savannah region of Kogi state, Nigeria.
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Books on the topic "Normalized Difference Water Index"

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Yengoh, Genesis T., David Dent, Lennart Olsson, Anna E. Tengberg, and Compton J. Tucker III. Use of the Normalized Difference Vegetation Index (NDVI) to Assess Land Degradation at Multiple Scales. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-24112-8.

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The Normalized Difference Vegetation Index. Oxford University Press, 2013.

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Dent, David, Genesis T. Yengoh, Lennart Olsson, Anna E. Tengberg, and Compton J. Tucker III. Use of the Normalized Difference Vegetation Index to Assess Land Degradation at Multiple Scales: Current Status, Future Trends, and Practical ... Springer, 2015.

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Service, United States Forest, ed. Greenness Products Normalized Difference Vegetation Index (NDVI), 1998, Data Archives, General Technical Report RMRS-GTR-27-CD, April 1999, Set of 5, (CD-ROM). [S.l: s.n., 1999.

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Book chapters on the topic "Normalized Difference Water Index"

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Hall, Dorothy K., and George A. Riggs. "Normalized-Difference Snow Index (NDSI)." In Encyclopedia of Earth Sciences Series, 779–80. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-90-481-2642-2_376.

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Zhang, Yaping, and Xu Chen. "Spatiotemporal Dynamics of Normalized Difference Vegetation Index in China Based on Remote Sensing Images." In Lecture Notes in Electrical Engineering, 1003–9. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-01273-5_113.

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Nicholson, Sharon E. "On the Use of the Normalized Difference Vegetation Index as an Indicator of Rainfall." In Global Precipitations and Climate Change, 293–305. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-642-79268-7_18.

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Mendes, Jorge Miguel, Vítor Manuel Filipe, Filipe Neves dos Santos, and Raul Morais dos Santos. "A Low-Cost System to Estimate Leaf Area Index Combining Stereo Images and Normalized Difference Vegetation Index." In Progress in Artificial Intelligence, 236–47. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30241-2_21.

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Rawat, Kishan S., Vinod Kumar Tripathi, Sudhir K. Singh, and Sushil K. Shukla. "Mapping of Normalized Difference Dispersal Index for Groundwater Quality Study on Parameter-Based Index for Irrigation: Kanchipuram District, India." In Field Practices for Wastewater Use in Agriculture, 239–60. Series statement: Innovations in agricultural and biological engineering: Apple Academic Press, 2021. http://dx.doi.org/10.1201/9781003034506-17.

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Tomasel, F. G., and J. M. Paruelo. "Normalized Difference Vegetation Index Estimation in Grasslands of Patagonia by ANN Analysis of Satellite and Climatic Data." In Artificial Neuronal Networks, 69–79. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-57030-8_5.

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Rivas-Tabares, David, and Ana M. Tarquis. "Towards Understanding Complex Interactions of Normalized Difference Vegetation Index Measurements Network and Precipitation Gauges of Cereal Growth System." In Complex Networks & Their Applications IX, 620–26. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-65347-7_51.

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Yengoh, Genesis T., David Dent, Lennart Olsson, Anna E. Tengberg, and Compton J. Tucker. "Introduction." In Use of the Normalized Difference Vegetation Index (NDVI) to Assess Land Degradation at Multiple Scales, 1–7. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24112-8_1.

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Yengoh, Genesis T., David Dent, Lennart Olsson, Anna E. Tengberg, and Compton J. Tucker. "Challenges to the Use of NDVI in Land Degradation Assessments." In Use of the Normalized Difference Vegetation Index (NDVI) to Assess Land Degradation at Multiple Scales, 55–56. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24112-8_10.

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Yengoh, Genesis T., David Dent, Lennart Olsson, Anna E. Tengberg, and Compton J. Tucker. "Recommendations for Future Application of NDVI." In Use of the Normalized Difference Vegetation Index (NDVI) to Assess Land Degradation at Multiple Scales, 57–59. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24112-8_11.

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Conference papers on the topic "Normalized Difference Water Index"

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Razali, Sheriza Mohd, and Ahmad Ainuddin Nuruddin. "Assessment of water content using remote sensing Normalized Difference Water Index: Preliminary study." In 2011 International Conference on Space Science and Communication (IconSpace). IEEE, 2011. http://dx.doi.org/10.1109/iconspace.2011.6015897.

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Gao, Bo-Cai. "Normalized difference water index for remote sensing of vegetation liquid water from space." In SPIE's 1995 Symposium on OE/Aerospace Sensing and Dual Use Photonics, edited by Michael R. Descour, Jonathan M. Mooney, David L. Perry, and Luanna R. Illing. SPIE, 1995. http://dx.doi.org/10.1117/12.210877.

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Yadav, Rahul, and Tara Chand. "Remote sensing to assess surface water quantity scenarios using normalized difference water index in the lesser Himalayan region." In Remote Sensing for Agriculture, Ecosystems, and Hydrology XXI, edited by Christopher M. Neale and Antonino Maltese. SPIE, 2019. http://dx.doi.org/10.1117/12.2533225.

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Zhang, Wenjiang, Xiaoming Cao, and Jian Peng. "Analyzing the 2007 drought of Poyang Lake Watershed with MODIS-derived normalized difference water deviation index." In Optical Engineering + Applications, edited by Wei Gao and Hao Wang. SPIE, 2008. http://dx.doi.org/10.1117/12.797552.

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Nugraha, Putu Virga Nanta, Sunu Wibirama, and Risanuri Hidayat. "River body extraction and classification using enhanced models of modified normalized water difference index at Yeh Unda River Bali." In 2018 International Conference on Information and Communications Technology (ICOIACT). IEEE, 2018. http://dx.doi.org/10.1109/icoiact.2018.8350789.

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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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Zhang, Xike, Qiuwen Zhang, Gui Zhang, and Zifan Gui. "A Comparison study of normalized difference water index and object-oriented classification method in river network extraction from landsat-tm imagery." In 2017 2nd International Conference on Frontiers of Sensors Technologies (ICFST). IEEE, 2017. http://dx.doi.org/10.1109/icfst.2017.8210502.

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Zhao, Tiebiao, David Doll, and YangQuan Chen. "<i>Better Almond Water Stress Monitoring Using Fractional-order Moments of Non-normalized Difference Vegetation Index</i>." In 2017 Spokane, Washington July 16 - July 19, 2017. St. Joseph, MI: American Society of Agricultural and Biological Engineers, 2017. http://dx.doi.org/10.13031/aim.201701593.

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Ramamurthy, Adinarayanane, and Anusha Roy. "Green and blue infrastucture to regulate thermal comfort in high density city planning. A case of Navi Mumbai, India." In 55th ISOCARP World Planning Congress, Beyond Metropolis, Jakarta-Bogor, Indonesia. ISOCARP, 2019. http://dx.doi.org/10.47472/amfc5106.

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Cities create an environment that is clearly distinct from their surrounding areas. Urban structures alter the surface energy budget, modify the vertical profile of various atmospheric properties, interact with both local and regional circulation, and introduce anthropogenic heat. As a result, the climate conditions in the urban environment significantly differ from their rural system. Sustainability in planning is a topic of high interest among urban planners, urbanist and policy makers yet lack of scientific knowledge in the field leads to low impact in evolving urban planning decisions. Urban climatic map, as a tool provides a visual and spatial information platform using Geographic Information System (GIS). Increase in vegetation and water surfaces, known as green and blue infrastructure (GBI), is of particular interest due to their multiple functionality and benefits for the urban environment, such as increasing urban biodiversity and improving air quality in case of urban vegetation. The urban climatic, environmental and planning parameters, as well as their impact, are considered to synthesize and comprehensively evaluate the physical urban environment with regard to thermal load and dynamic potential. The parameters considered to evaluate Thermal load include: Topography; Population Density; Land Surface Temperature; Air Temperature and Dynamic potential are: Normalized difference Built up Index; Normalized difference Vegetation Index; Normalized difference Water Index and Prevailing Wind of the study region. Study concludes with planning decisions to develop urban climatology-based map for GBI to enhance cooling effects and thereby undertaking measures to regulate thermal comfort in the city through green and blue infrastructure.
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Zhao, Tiebiao, Brandon Stark, YangQuan Chen, Andrew L. Ray, and David Doll. "A detailed field study of direct correlations between ground truth crop water stress and normalized difference vegetation index (NDVI) from small unmanned aerial system (sUAS)." In 2015 International Conference on Unmanned Aircraft Systems (ICUAS). IEEE, 2015. http://dx.doi.org/10.1109/icuas.2015.7152331.

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Reports on the topic "Normalized Difference Water Index"

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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.), October 2018. http://dx.doi.org/10.21079/11681/29517.

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Becker, Sarah, Megan Maloney, and Andrew Griffin. A multi-biome study of tree cover detection using the Forest Cover Index. Engineer Research and Development Center (U.S.), September 2021. http://dx.doi.org/10.21079/11681/42003.

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Tree cover maps derived from satellite and aerial imagery directly support civil and military operations. However, distinguishing tree cover from other vegetative land covers is an analytical challenge. While the commonly used Normalized Difference Vegetation Index (NDVI) can identify vegetative cover, it does not consistently distinguish between tree and low-stature vegetation. The Forest Cover Index (FCI) algorithm was developed to take the multiplicative product of the red and near infrared bands and apply a threshold to separate tree cover from non-tree cover in multispectral imagery (MSI). Previous testing focused on one study site using 2-m resolution commercial MSI from WorldView-2 and 30-m resolution imagery from Landsat-7. New testing in this work used 3-m imagery from PlanetScope and 10-m imagery from Sentinel-2 in imagery in sites across 12 biomes in South and Central America and North Korea. Overall accuracy ranged between 23% and 97% for Sentinel-2 imagery and between 51% and 98% for PlanetScope imagery. Future research will focus on automating the identification of the threshold that separates tree from other land covers, exploring use of the output for machine learning applications, and incorporating ancillary data such as digital surface models and existing tree cover maps.
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Manninen, Terhikki, and Pauline Stenberg. Influence of forest floor vegetation on the total forest reflectance and its implications for LAI estimation using vegetation indices. Finnish Meteorological Institute, 2021. http://dx.doi.org/10.35614/isbn.9789523361379.

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Recently a simple analytic canopy bidirectional reflectance factor (BRF) model based on the spectral invariants theory was presented. The model takes into account that the recollision probability in the forest canopy is different for the first scattering than the later ones. Here this model is extended to include the forest floor contribution to the total forest BRF. The effect of the understory vegetation on the total forest BRF as well as on the simple ratio (SR) and the normalized difference (NDVI) vegetation indices is demonstrated for typical cases of boreal forest. The relative contribution of the forest floor to the total BRF was up to 69 % in the red wavelength range and up to 54 % in the NIR wavelength range. Values of SR and NDVI for the forest and the canopy differed within 10 % and 30 % in red and within 1 % and 10 % in the NIR wavelength range. The relative variation of the BRF with the azimuth and view zenith angles was not very sensitive to the forest floor vegetation. Hence, linear correlation of the modelled total BRF and the Ross-thick kernel was strong for dense forests (R2 > 0.9). The agreement between modelled BRF and satellite-based reflectance values was good when measured LAI, clumping index and leaf single scattering albedo values for a boreal forest were used as input to the model.
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Normalized Difference Vegetation Index for Fanno Creek, Oregon. US Geological Survey, 2011. http://dx.doi.org/10.3133/70046613.

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