Academic literature on the topic 'NDVI (Normalized Difference Vegetation Index)'
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Journal articles on the topic "NDVI (Normalized Difference Vegetation Index)"
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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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Sharmin Siddika, Md Nazmul Haque, and Mizbah Ahmed Sresto. "Assessing the Relationship among the Land Surface Features: A Geographic Information System (GIS) and Remote Sensing (RS) Based Approach for City Area." Journal of Applied Science & Process Engineering 8, no. 2 (2021): 935–52. http://dx.doi.org/10.33736/jaspe.3616.2021.
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Due to climate change and urbanization, it is important to monitor and evaluate the components of the environment. For this reason, ward-22 and ward-27 of the Khulna City Corporation (KCC) area have been selected for the study. This research seeks to identify the existing land use profile and assess the land surface components such as topography, Normalized Difference Buildup Index (NDBI), Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI), Normalized Difference Salinity Index (NDSI) and Land Surface Temperature (LST) to measure the relationships among the land surface components. The land use land cover map shows that about 59% of ward-22 and 71.5% area of ward-27 are built-up areas. Both of the wards contain little amount of water body, vegetation and open space. Both of the wards have residential land use types with commercial purposes on the periphery. Accordingly, 63.32% and 65% of structures of ward-22 and 27 are pucca. The land surface components reveal that both areas contain lower slopes, less vegetation, less moisture, severe salinity, highly built-up areas, and high land surface temperature. The relationships among the land surface components show that NDVI has a negative relation with LST and NDBI whereas NDVI represents a positive correlation with NDMI. On the other hand, NDBI shows a positive correlation with LST whereas NDMI negatively correlates with LST. NDSI and topography reflect no meaningful relationship between NDBI, NDVI, LST, and NDMI. However, the research findings may be essential to city planners and decision-makers for incorporating better urban management at the micro level concerning climate change.
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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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Selka, Imene, Abderahemane Medjdoub Mokhtari, Kheira Anissa Tabet Aoul, Djamal Bengusmia, Kacemi Malika, and Khadidja El-Bahdja Djebbar. "Assessing the Impact of Land Use and Land Cover Changes on Surface Temperature Dynamics Using Google Earth Engine: A Case Study of Tlemcen Municipality, Northwestern Algeria (1989–2019)." ISPRS International Journal of Geo-Information 13, no. 7 (2024): 237. http://dx.doi.org/10.3390/ijgi13070237.
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Changes in land use and land cover (LULC) have a significant impact on urban planning and environmental dynamics, especially in regions experiencing rapid urbanization. In this context, by leveraging the Google Earth Engine (GEE), this study evaluates the effects of land use and land cover modifications on surface temperature in a semi-arid zone of northwestern Algeria between 1989 and 2019. Through the analysis of Landsat images on GEE, indices such as normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), and normalized difference latent heat index (NDLI) were extracted, and the random forest and split window algorithms were used for supervised classification and surface temperature estimation. The multi-index approach combining the Normalized Difference Tillage Index (NDTI), NDBI, and NDVI resulted in kappa coefficients ranging from 0.96 to 0.98. The spatial and temporal analysis of surface temperature revealed an increase of 4 to 6 degrees across the four classes (urban, barren land, vegetation, and forest). The Google Earth Engine approach facilitated detailed spatial and temporal analysis, aiding in understanding surface temperature evolution at various scales. This ability to conduct large-scale and long-term analysis is essential for understanding trends and impacts of land use changes at regional and global levels.
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Cao, Ruyin, Yan Feng, Xilong Liu, Miaogen Shen, and Ji Zhou. "Uncertainty of Vegetation Green-Up Date Estimated from Vegetation Indices Due to Snowmelt at Northern Middle and High Latitudes." Remote Sensing 12, no. 1 (2020): 190. http://dx.doi.org/10.3390/rs12010190.
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Vegetation green-up date (GUD), an important phenological characteristic, is usually estimated from time-series of satellite-based normalized difference vegetation index (NDVI) data at regional and global scales. However, GUD estimates in seasonally snow-covered areas suffer from the effect of spring snowmelt on the NDVI signal, hampering our realistic understanding of phenological responses to climate change. Recently, two snow-free vegetation indices were developed for GUD detection: the normalized difference phenology index (NDPI) and normalized difference greenness index (NDGI). Both were found to improve GUD detection in the presence of spring snowmelt. However, these indices were tested at several field phenological camera sites and carbon flux sites, and a detailed evaluation on their performances at the large spatial scale is still lacking, which limits their applications globally. In this study, we employed NDVI, NDPI, and NDGI to estimate GUD at northern middle and high latitudes (north of 40° N) and quantified the snowmelt-induced uncertainty of GUD estimations from the three vegetation indices (VIs) by considering the changes in VI values caused by snowmelt. Results showed that compared with NDVI, both NDPI and NDGI improve the accuracy of GUD estimation with smaller GUD uncertainty in the areas below 55° N, but at higher latitudes (55°N-70° N), all three indices exhibit substantially larger GUD uncertainty. Furthermore, selecting which vegetation index to use for GUD estimation depends on vegetation types. All three indices performed much better for deciduous forests, and NDPI performed especially well (5.1 days for GUD uncertainty). In the arid and semi-arid grasslands, GUD estimations from NDGI are more reliable (i.e., smaller uncertainty) than NDP-based GUD (e.g., GUD uncertainty values for NDGI vs. NDPI are 4.3 d vs. 7.2 d in Mongolia grassland and 6.7 d vs. 9.8 d in Central Asia grassland), whereas in American prairie, NDPI performs slightly better than NDGI (GUD uncertainty for NDPI vs. NDGI is 3.8 d vs. 4.7 d). In central and western Europe, reliable GUD estimations from NDPI and NDGI were acquired only in those years without snowfall before green-up. This study provides important insights into the application of, and uncertainty in, snow-free vegetation indices for GUD estimation at large spatial scales, particularly in areas with seasonal snow cover.
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Табунщик, В. А., Е. А. Петлюкова та М. О. Хитрин. "Применение спутниковых снимков Sentinel- 2 для анализа земель используемых в сельском хозяйстве (на примере Раздольненского района Республики Крым)". Труды Карадагской научной станции им. Т.И. Вяземского - природного заповедника РАН, № 1 (5) (8 квітня 2021): 43–57. http://dx.doi.org/10.21072/eco.2021.05.05.
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В статье рассматривается понятие о вегетационных индексах (Ratio Vegetation Index, Infrared Percentage Vegetation Index, Difference Vegetation Index, Soil adjusted vegetation index, Normalized Difference Vegetation Index) и приводится теория и методика использования космических снимков Sentinel для расчета значений Normalized Difference Vegetation Index (NDVI). Для территории Раздольненского района Республики Крым изучены значения Normalized Difference Vegetation Index (NDVI) за период с 16 февраля 2017 г. по 17 октября 2017 г., на основании открытых космических снимков Sentinel (Sentinel-2A и Sentinel-2В). Анализируются минимальные, максимальные и средние значения NDVI за рассматриваемы период, а также распределение территории Раздольненского района Республики Крым по различным диапазонам дискретной шкалы NDVI за рассматриваемый период (в процентах от общей площади района). На основании значений NDVI рассматривается использование земель Раздольненского района в сельскохозяйственной деятельности.
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Filho, Júlio Cezar Cotrim Moreira, and João Rodrigues Tavares Junior. "Avaliação da precisão temática de composições de NDBI, NDVI, NDWI." Revista Brasileira de Geomática 4, no. 1 (2016): 3. http://dx.doi.org/10.3895/rbgeo.v4n1.5462.
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Este artigo trata de experimentos usando composições de índices físicos NDVI (Normalized Difference Vegetation Index), NDWI (Normalized Difference Water Index), NDBI (Normalized Difference Built-Up Index), para avaliar a precisão temática do classificador Máxima Verossimilhança (MAXVER) usando exatidão global, índice kappa e teste Z. A área de estudo foi o entorno da Lagoa Olho D’Água localizada em Jaboatão dos Guararapes-PE. Foram usadas duas cenas TM LANDSAT-5, órbita-ponto 214-066, de 17/03/20111 e 29/09/2011, do DGI-INPE (Divisão de Geração de Imagens do Instituto Nacional de Pesquisas Espaciais), e todo o processamento realizado no SPRING 5.0.6. Os resultados indicam que apenas usando índices físicos substituindo composições RGB, a acurácia temática é muito degradada. A classificação MAXVER da composição NDBI-TM4-TM-3 e IHS obtiveram bons resultados em acurácia temática, demonstrando que o método de combinações de índices físicos e composições RGB podem ser usados para melhorar os resultados da classificação MAXVER.
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Xu, Jingyi, Yao Tang, Jiahui Xu, et al. "Evaluation of Vegetation Indexes and Green-Up Date Extraction Methods on the Tibetan Plateau." Remote Sensing 14, no. 13 (2022): 3160. http://dx.doi.org/10.3390/rs14133160.
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The vegetation green-up date (GUD) of the Tibetan Plateau (TP) is highly sensitive to climate change. Accurate estimation of GUD is essential for understanding the dynamics and stability of terrestrial ecosystems and their interactions with climate. The GUD is usually determined from a time-series of vegetation indices (VIs). The adoption of different VIs and GUD extraction methods can lead to different GUDs. However, our knowledge of the uncertainty in these GUDs on TP is still limited. In this study, we evaluated the performance of different VIs and GUD extraction methods on TP from 2003 to 2020. The GUDs were determined from six Moderate Resolution Imaging Spectroradiometer (MODIS) derived VIs: normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), normalized difference infrared index (NDII), phenology index (PI), normalized difference phenology index (NDPI), and normalized difference greenness index (NDGI). Four extraction methods (βmax, CCRmax, G20, and RCmax) were applied individually to each VI to determine GUD. The GUDs obtained from all VIs showed similar patterns of early green-up in the eastern and late green-up in the western plateau, and similar trend of GUD advancement in the eastern and postponement in the western plateau. The accuracy of the derived GUDs was evaluated by comparison with ground-observed GUDs from 19 agrometeorological stations. Our results show that two snow-free VIs, NDGI and NDPI, had better performance in GUD extraction than the snow-calibrated conventional VIs, NDVI and EVI. Among all the VIs, NDGI gave the highest GUD accuracy when combined with the four extraction methods. Based on NDGI, the GUD extracted by the CCRmax method was found to have the highest consistency (r = 0.62, p < 0.01, RMSE = 11 days, bias = −3.84 days) with ground observations. The NDGI also showed the highest accuracy for preseason snow-covered site-years (r = 0.71, p < 0.01, RMSE = 10.69 days, bias = −4.05 days), indicating its optimal resistance to snow cover influence. In comparison, NDII and PI hardly captured GUD. NDII was seriously affected by preseason snow cover, as indicated by the negative correlation coefficient (r = −0.34, p < 0.1), high RMSE and bias (RMSE = 50.23 days, bias = −24.25 days).
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Tang, Yao, Jin Chen, Jingyi Xu, et al. "The Impact of Autumn Snowfall on Vegetation Indices and Autumn Phenology Estimation." Remote Sensing 16, no. 24 (2024): 4783. https://doi.org/10.3390/rs16244783.
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Monitoring autumn vegetation dynamics in alpine regions is crucial for managing local livestock, understanding regional productivity, and assessing the responses of alpine regions to climate change. However, remote sensing-based vegetation monitoring is significantly affected by snowfall. The impact of autumn snowfall, particularly when vegetation has not fully entered dormancy, has been largely overlooked. To demonstrate the uncertainties caused by autumn snowfall in remote sensing-based vegetation monitoring, we analyzed 16 short-term snowfall events in the Qinghai–Tibet Plateau. We employed a synthetic difference-in-differences estimation framework and conducted simulated experiments to isolate the impact of snowfall from other factors, revealing its effects on vegetation indices (VIs) and autumn phenology estimation. Our findings indicate that autumn snowfall notably affects commonly used VIs and their associated phenology estimates. Modified VIs (i.e., Normalized Difference Infrared Index (NDII), Phenology Index (PI), Normalized Difference Phenology Index (NDPI), and Normalized Difference Greenness Index (NDGI)) revealed greater resilience to snowfall compared to conventional VIs (i.e., Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI)) in phenology estimation. Areas with remaining green vegetation in autumn showed more pronounced numerical changes in VIs due to snowfall. Furthermore, the impact of autumn snowfall closely correlated with underlying vegetation types. Forested areas experienced less impact from snowfall compared to grass- and shrub-dominated regions. Earlier snowfall onset and increased snowfall frequency further exacerbated deviations in estimated phenology caused by snowfall. This study highlights the significant impact of autumn snowfall on remote sensing-based vegetation monitoring and provides a scientific basis for accurate vegetation studies in high-altitude regions.
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Fitri, Mutiara, and Triyatno Triyatno. "UTILIZATION OF REMOTE SENSING FOR LAND SURFACE TEMPERATURE (LST) DISTRIBUTION MAPPING IN SOLOK CITY IN 2021." International Remote Sensing Applied Journal 2, no. 1 (2023): 20–30. http://dx.doi.org/10.24036/irsaj.v2i1.22.
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Solok City is one of the cities in West Sumatra which has a fairly rapid population growth, this has led to an increase in development and a decrease in green open land or vegetation land. This affects the ground surface which absorbs and reflects more of the sun's heat. These conditions have an impact on rising surface temperatures. This research was conducted to analyze changes in vegetation land, built-up land and changes in surface temperature in Solok City using Landsat-8 Imagery of Solok City in 2015 and 2021 using the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDVI) algorithm models. (NDBI) and Land Surface Temperature (LST). The results of the study explain that the normalized difference vegetation index (NDVI) in Solok City has decreased, in 2015 the area of vegetation density was 2344 Ha and in 2021 it was reduced to 1888 Ha. This is in line with the increase in building area / Normalized Difference Built-up Index (NDBI) in 2015, namely 1 from 921 Ha to 2295 Ha in 2021. Reduced vegetation area and increased built-up area increased Land Surface Temperature (LST) in the area. research, the temperature in 2015 was around 32.9° C and in 2021 there was an increase in surface temperature to 33.6° C. Pearson product-moment correlation was carried out to see the level of relationship between LST and NDVI and NDBI.
Dissertations / Theses on the topic "NDVI (Normalized Difference Vegetation Index)"
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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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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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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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Bradford, Jessica. "Examining Culex tarsalis (Diptera: Culicidae) population changes with satellite vegetation index data." Kansas State University, 2014. http://hdl.handle.net/2097/17139.
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Master of Public Health<br>Department of Diagnostic Medicine/Pathobiology<br>Michael W. Sanderson<br>A zoonotic disease is any disease or infection that is naturally transmissible from vertebrate animals to humans. Over 200 zoonoses have been described (Zoonoses and the Human-Animal-Ecosystems Interface, 2013). Many zoonotic viruses are arboviruses, viruses transmitted by an infected, blood-sucking, arthropod vector (Hunt, 2010). There are several endemic arboviruses in the United States; some foreign arboviruses, such as Rift Valley fever (RVF) virus, are potential bioterrorism agents (Dar, 2013). Arboviruses, both endemic and foreign, threaten public health (Gubler, 2002) and therefore disease surveillance, vector control and public education are all vital steps in minimizing arboviral disease impact in the United States.
Mosquito-borne disease threats, such as West Nile virus and Rift Valley fever, are constant concerns in the United States and globally. Current strategies to prevent and control mosquito-borne diseases utilize vector distribution, seasonal and daylight timing, and variation in population numbers. Climate factors, such as availability of still water for development of immature mosquitoes, shade, and rainfall, are known to influence population dynamics of mosquitoes. Using 1995-2011 mosquito population surveillance data from Fort Riley, Kansas, we compared population numbers of Culex tarsalis (Diptera: Culicidae), a vector of several arboviruses including West Nile virus and potentially Rift Valley fever, to a satellite-derived index of climate, the Normalized Difference Vegetation Index (NDVI) anomaly. No correlation between the population numbers and NDVI anomaly was observed, which contrasts with results from similar analyses in other locations. These findings suggest a need for continued investigation into mosquito population dynamics in additional ecological regions of the United States to better describe the heterogeneity of environment-population relationships within and among mosquito species.
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Jesus, Bruna Luiza Pereira de. "A relação entre a temperatura radiométrica de superfície (Land Surface Temperature-LST), índice de vegetação (Normalizes Diference Vegetation Index-NDVI) e os diferentes padrões de uso da terra do município de São Paulo." Universidade de São Paulo, 2015. http://www.teses.usp.br/teses/disponiveis/8/8135/tde-11012016-143102/.
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Esse trabalho tem como objetivo compreender as relações entre a Land Surface Temperature (LST), Normalized Difference Vegetation Índex (NDVI) e os padrões do uso da terra do município de São Paulo no período de 1985 a 2010. Analisou-se 15 bairros, nos quais foram extraídas 45 amostras aleatórias de diferentes padrões de uso da terra; subdivididas em baixo padrão, médio padrão e médio alto padrão. Com o aporte de geotecnologia, foi feita a extração dos dados das imagens de satélite Landsat 5 (TM) e das Ortofotos do ano de 2010. O comportamento das amostras variou de acordo como os diferentes perfis dos grupos analisados. O grupo de baixo padrão foi o que apresentou as maiores amplitudes térmicas, ausência de arborização urbana atreladas a um baixo padrão construtivo. O grupo de médio padrão é caracterizado pela predominância de área verticalizada e apresenta uma arborização urbana escassa em meio a uma malha urbana consolidada. O grupo de médio alto padrão foi o que mais apresentou arborização urbana, distribuída de forma homogênea na maioria das amostras, portanto foi o grupo que teve baixas amplitudes térmicas e o índice de Normalized Difference Vegetation Index (NDVI) com pouca variação. Os testes mostraram fortes correlações negativas entre as amostras de Land Surface Temperature (LST) e o índice de Normalized Difference Vegetation Index (NDVI), sendo -0,58 em 1985, -0,43 em 2004 e -0,82 em 2010. Os diferentes padrões de uso da terra, relacionados à temperatura de superfície, e o índice de vegetação, aliado à preocupação com o planejamento ambiental, deve resultar na melhoria da qualidade de vida da população. Esta pesquisa faz parte do Projeto Temático processo FAPESP 08/58161 -1, \"Assessment of Impacts and Vulnerability to Climate Change in Brazil and strategies for Adaptation options\", Component 5: Vulnerability of the metropolitan region of São Paulo to climate Change.<br>This study aims to understand the relationship between Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI) and the patterns of land use in the municipality of São Paulo, from 1985 to 2010. A totoal of 45 random samples were extracted from the 15 districts used in this study, with different patterns of land use which were subdivided into three different clases: low-end, middle and middle-high. Geospatial approaches allowed the extraction of satellite image data from Landsat 5 data (TM) and from Orthophotos from 2010. The behavior of the samples varied accordingly to the different group profiles. The low-end group presented the highest thermal amplitudes and more significant absence of urban vegetation linked, both to low urbanization and construction standards. The average standard group is characterized by the predominance of vertical buildings and lacks urban trees amidst a consolidated urban landscape. The average-high standard group displayed the highest concentration of green urban areas, distributed homogeneously in most samples, so this group presented low variations both in temperature amplitude and in the Normalized Difference Vegetation Index (NDVI). The correlation tests showed strong negative correlations between samples of Land Surface Temperature (LST) and the NDVI samples, of -0.58 in 1985, -0.43 in 2004 and -0.82 in 2010. Understanding the relations between the different patterns of land use, surface temperature and the NDVI (with due concern for environmental planning) is an important step in the identification and rehabilitation of enviromentally. This research is part of the Thematic Project FAPESP 08/58161 -1 process, \"Assessment of Impacts and Vulnerability to Climate Change in Brazil and strategies for Adaptation options\", Component 5: Vulnerability of the metropolitan region of São Paulo to climate Change.
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Meneses, Kamila Cunha de. "Fluorescência induzida pelo sol, xco2 e ndvi em cana-de-açúcar do Centro-Sul do Brasil /." Jaboticabal, 2018. http://hdl.handle.net/11449/153735.
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Orientador: Glauco de Souza Rolim<br>Coorientador: Newton La Scala Júnior<br>Banca: Jansle Vieira Rocha<br>Banca: Teresa Cristina Tarle Pissarra<br>Resumo: O sensoriamento remoto é uma ferramenta importante no monitoramento e na previsão da qualidade e da quantidade de cultivos. Nos últimos anos, surgiram satélites com melhores resoluções espaciais, temporais e, principalmente, novos produtos, como a fluorescência da clorofila induzida pelo sol (SIF), a coluna média de CO2 atmosférico (XCO2), além do Índice da Vegetação da Diferença Normalizada (NDVI). A cana-de-açúcar é o principal cultivo para a produção de açúcar e bioenergia no mundo. A hipótese deste estudo é que o SIF e o XCO2 têm correlação com a taxa fotossintética, e o NDVI avalia o vigor do dossel ligado à biomassa verde. O estudo teve como objetivo analisar a relação entre SIF, Xco2 e NDVI com a produtividade e o nível de açúcar da cana-de-açúcar. O estudo foi realizado em locais representativos de uma das principais regiões de produção de cana-de-açúcar do mundo, na região Centro-Sul do Brasil. Quatro locais de estudo foram identificados para representar a região, sendo Pradópolis, Araraquara e Iracemápolis, no Estado de São Paulo, e Quirinópolis, no Estado Goiás, Brasil. Os dados foram coletados no período de 2015 a 2016, em sistemas de dados orbitais. Para as análises meteorológicas, foram utilizados dados diários ajustados na escala mensal. Os dados de toneladas de cana por hectare (TCH, em t ha-1) e o açúcar total recuperável (TSR, em kg t-1) foram coletados de talhões de cana- -de-açúcar de empresas da região e estratificados por nível de município, no período e... (Resumo completo, clicar acesso eletrônico abaixo)<br>Abstract: Remote sensing is an important tool for monitoring and forecasting the quality and quantity of crops. In recent years, satellites with better spatial and temporal resolutions and, mainly, new products, such as the S olar - induced c hlorophyll F luorescence (SIF), the average column of atmospheric CO 2 and normalized difference vegetation index. The Sugarcane is the main crop for sugar and bioenergy production in the world. Our hypothesis is that SIF and Xco 2 have a correlation with the photosynt hetic rate and NDVI evaluate the canopy vigor, linked to the green biomass of crops. Thus, the objective of this study was to analyze the relationship between SIF, Xco 2, and NDVI with sugarcane yield and sugar level. The study was conducted in representati ve locations of one of the main regions of sugarcane production in the world, in the C enter - S outh region of Brazil. Four study locations were identified to represent the region being, Pradópolis, Araraquara, and Iracemápolis in the São Paulo (SP) state and Quirinópolis in the Goiás (GO) state, Brazil. The data were collected in the period from 2015 to 2016 in orbital data systems. The meteorological analyses were used daily data adjusted in the monthly scale. The data of tons of cane per hectare (TCH, in t ha - 1 ) and total sugar recovery (TSR, in kg t - 1 ) were collected from sugarcane plots of companies in the region and stratified by level of the municipality in the period between April and November of the growing seaso... (Complete abstract click electronic access below)<br>Mestre
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Jorge, Catarina Tonelo. "Phenology analysis in a cork oak woodland through digital photography and spectral vegetation indexes." Master's thesis, ISA, 2019. http://hdl.handle.net/10400.5/19543.
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Mestrado em Engenharia do Ambiente - Instituto Superior de Agronomia<br>Digital repeat photography is a method to monitor the phenology of vegetation that has gained momentum this past decade. As a result, the need for further case-studies is required.
This work aims to prove that it is possible to use digital cameras instead of spectral information to track phenology in a Mediterranean cork oak woodland. The photos will originate the green chromatic coordinates (GCC) index while the normalized difference vegetation index (NDVI) derives from the spectral data collected with a field spectroradiometer.
The results were found by employing a regular commercial camera to take monthly pictures along with the spectral measurements. They showed good agreement among methods especially for the herbaceous layer whose GCC had a very good fit with NDVI. The coefficient of determination for the herbaceous layer, the shrub cistus and shrub ulex was 0.89, 0.62 and 0.30, respectively. However, these regressions may be improved upon by grouping the shrub species.
The shrubs had a lower correlation between the two indices and all three groups showed a response to water availability. For these reasons, a linear regression between GCC and the normalized water difference index (NDWI) was pursued. This second regression showed better results for shrubs, with coefficients of determination of 0.78 e 0.55, respectively, and a similar value for the herbaceous layer (0.84).
The herbaceous layer was found to react quickly to water. Because it only has access to superficial water, its phenology is dependent on precipitation. This group had a good outcome with more long-term observations than shrubs (eight years of data vs. three years). So, it would be the most suitable plant functional type to be tracked using the digital repeat photography method coupled with GCC. Nonetheless, using photos and GCC proves to have the potential to monitor a wide spectrum of vegetation types<br>N/A
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Semel, Brandon Pierce. "Factors affecting golden-crowned sifaka (Propithecus tattersalli) densities and strategies for their conservation." Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/102781.
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Habitat degradation and hunting pose the most proximate threats to many primate species, while climate change is expected to exacerbate these threats (habitat and climate change combined henceforth as "global change") and present new challenges. Madagascar's lemurs are earth's most endangered primates, placing added urgency to their conservation in the face of global change. My dissertation focused on the critically endangered golden-crowned sifaka (Propithecus tattersalli; hereafter, "sifaka") which is endemic to fragmented forests across a gradient of dry, moderate, and wet forest types in northeastern Madagascar. I surveyed sifakas across their global range and investigated factors affecting their densities. I explored sifaka diets across different forest types and evaluated if nutritional factors influenced sifaka densities. Lastly, I investigated sifaka range-wide genetic diversity and conducted a connectivity analysis to prioritize corridor-restoration and other potential conservation efforts. Sifaka densities varied widely across forest fragments (6.8 (SE = 2.0-22.8) to 78.1 (SE = 53.1-114.8) sifakas/km2) and populations have declined by as much as 30-43% in 10 years, from ~18,000 to 10,222-12,631 individuals (95% CI: 8,230-15,966). Tree cutting, normalized difference vegetation index (NDVI) during the wet season, and Simpson's diversity index (1-D) predicted sifaka densities range-wide. Sifakas consumed over 101 plant species and spent 27.1% of their active time feeding on buds, flowers, fruits, seeds, and young and mature leaves. Feeding effort and plant part consumption varied by season, forest type, and sex. Minerals in sifaka food items (Mg (β = 0.62, SE = 0.19) and K (β = 0.58, SE = 0.20)) and wet season NDVI (β = 0.43, SE = 0.20) predicted sifaka densities. Genetic measures across forest fragments indicated that sifaka populations are becoming more isolated (moderate FIS values: mean = 0.27, range = 0.11-0.60; high M-ratios: mean = 0.59, range = 0.49-0.82; low overall effective population size: Ne = 139.8-144 sifakas). FST comparisons between fragments (mean = 0.12, range = 0.01-0.30) supported previous findings that sifakas still moved across the fragmented landscape. Further validation of these genetic results is needed. I identified critical corridors that conservation managers could protect and/or expand via active reforestation to ensure the continued existence of this critically-endangered lemur.<br>Doctor of Philosophy<br>Worldwide, many species of primates are threatened with extinction due to habitat degradation,
hunting, and climate change (habitat and climate combined threats, henceforth, "global change").
These threats work at different time scales, with hunting being the most immediate and climate
change likely to have its fullest impact experienced from the present to a longer time frame.
Lemurs are a type of primate found only on Madagascar, an island experiencing rapid global
change, which puts lemurs at a heighted risk of extinction. My dissertation research focused on
the critically endangered golden-crowned sifaka (Propithecus tattersalli; hereafter, "sifaka"), a
species of lemur found only in a few isolated forests across a dry to wet gradient in northeastern
Madagascar. To better understand their extinction risk, I conducted surveys to estimate the
number of sifakas remaining and investigated several factors that might determine how many
sifakas can live in one place. I then explored how sifaka diets varied depending on the forest type
that they inhabit and tested whether nutrients in their food might determine sifaka numbers.
Lastly, I calculated sifaka genetic diversity to assess their ability to adapt to new environmental
conditions and to determine whether sifakas can move across the landscape to find new mates
and to potentially colonize new areas of habitat. Sifaka densities varied widely across their range
(6.8-78.1 sifakas/km2
). Only 10,222-12,631 sifakas remain, which is 30-43% less than the range
of estimates obtained 10 years ago (~18,000 sifakas). Tree cutting, normalized difference
vegetation index (NDVI; a measure of plant health or "greenness" obtained from satellite data),
and a tree species diversity index were useful measures to predict sifaka densities. Sifakas ate different plant parts (buds, flowers, fruits, seeds, and leaves) from over 101 plant species. The
amount of time they spent eating each day varied by the time of year, forest type, and sex. On
average, they spent a quarter of their day eating. Magnesium and potassium concentrations in
sifaka food items also were useful nutrition-related measures to predict sifaka densities. Genetic
analyses suggested that sifaka populations are becoming more isolated and inbred, meaning
sifakas are breeding with other sifakas to which they are closely related. However, it appears that
sifakas still can move between forest patches to find new mates and to potentially colonize new
areas, if such areas are created. Further validation of these genetic results is needed. I also
identified critical areas that will be important to protect and reforest to ensure that movements
between populations can continue.
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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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Wilfong, Bryan N. "Detecting an invasive shrub in deciduous forest understories using remote sensing." Oxford, Ohio : Miami University, 2008. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=miami1217288997.
Full textBooks on the topic "NDVI (Normalized Difference Vegetation 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. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-24112-8.
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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.n., 1999.
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Pettorelli, Nathalie. Normalized Difference Vegetation Index. Oxford University Press, 2013.
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Normalized Difference Vegetation Index. Oxford University Press, Incorporated, 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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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 Considerations. Springer, 2015.
Find full textBook chapters on the topic "NDVI (Normalized Difference Vegetation Index)"
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Zapata, Francisco, Eric Smith, Vladik Kreinovich, and Nguyen Hoang Phuong. "Why Normalized Difference Vegetation Index (NDVI)?" In Biomedical and Other Applications of Soft Computing. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08580-2_9.
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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. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24112-8_11.
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Nahin, Khadiza Tul Kobra, Hasna Hena Sara, Krishna Rani Barai, Zahidul Quayyum, and Jill Baumgartner. "Spatiotemporal Variability of Urban Greenspace and Surface Temperature in Dhaka City: A Public Health Aspect." In S.M.A.R.T. Environments. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-32840-4_7.
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AbstractUrban greenspaces can affect the physical and mental health of city residents and they can also contribute to improving urban environmental quality in ways that can benefit human health. Dhaka, a megacity with over 22.4 million residents, has progressively lost its greenspace over the past decade as the city has grown and urbanised. This study evaluates the availability and accessibility of greenspace considering its population and residential areas, as well as assessing the changes to greenspace in the last 30 years throughout the city. We utilized secondary data from the 2011 Census and areal imagery to perform the analysis for city wards, the smallest administrative unit, using ArcGIS software. We generated geospatial maps of greenspace distribution and accessibility as well as vegetation, land surface temperature and humidity in different years. Accessibility to greenspace was measured with 100-meter and 300-meter buffer zones, and a total of 56.5 square kilometers area of 77.47 square kilometers of residential area fell under these territories. Changes in vegetation were obtained using Normalized Difference Vegetation Index (NDVI) for the years 1990, 2000, 2010, and 2020, and a high level of loss in vegetation was observed. Land Surface Temperature (LST) and Normalized Difference Moisture Index (NDMI) were used to assess the temperature and humidity for the same years. We measured that Dhaka has 2.24% greenspace coverage and only 2 wards out of 110 have greater than 20% greenspace coverage. A highest estimate of 0.003207 square meter per capita greenspace was found at ward-46, which does not even meet the minimum health standard. Increased temperature and decreased humidity were observed in Dhaka city from 1990 to 2020, in a level that may adversely impact on the city population’s public health. We found a high correlation between NDVI with LST and NDMI. In 49% of wards, vegetation and humidity decreased, whereas temperature increased. This study provides noteworthy information on the lack of greenspace throughout Dhaka city. The spatial distribution of greenspace provided in the study has the potential to be useful in taking measures for improving sustainable greenery management in the city area and the health of Dhaka’s growing population.
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Chiu, Chenyuan, Fei Shen, Peisong Zhuang, Xinlong Zhang, Shichao Chen, and Jialong Wen. "Spot-5 Landslide Interpretation of Normalized Difference Vegetation Index (NDVI) Satellite Imagery." In Atlantis Highlights in Engineering. Atlantis Press International BV, 2024. http://dx.doi.org/10.2991/978-94-6463-435-8_28.
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Yengoh, Genesis T., David Dent, Lennart Olsson, Anna E. Tengberg, and Compton J. Tucker. "Applications of NDVI for Land Degradation Assessment." In Use of the Normalized Difference Vegetation Index (NDVI) to Assess Land Degradation at Multiple Scales. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24112-8_3.
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Yengoh, Genesis T., David Dent, Lennart Olsson, Anna E. Tengberg, and Compton J. Tucker. "Main Global NDVI Datasets, Databases, and Software." In Use of the Normalized Difference Vegetation Index (NDVI) to Assess Land Degradation at Multiple Scales. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24112-8_8.
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Gaikwad, Sandeep, Karbhari Kale, Rahul Chawda, and Kanubhai Patel. "UAV-Based Crop Health Analysis Using the Normalized Difference Vegetation Index (NDVI) Method." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-1326-4_14.
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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. 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. "Limits to the Use of NDVI in Land Degradation Assessment." In Use of the Normalized Difference Vegetation Index (NDVI) to Assess Land Degradation at Multiple Scales. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24112-8_4.
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Yengoh, Genesis T., David Dent, Lennart Olsson, Anna E. Tengberg, and Compton J. Tucker. "Key Issues in the Use of NDVI for Land Degradation Assessment." In Use of the Normalized Difference Vegetation Index (NDVI) to Assess Land Degradation at Multiple Scales. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24112-8_5.
Full textConference papers on the topic "NDVI (Normalized Difference Vegetation Index)"
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Roque, Victório Mariani, Tiago Oliveira Weber, and Altamiro Amadeu Susin. "Microcontrolled System to Map and Monitor Normalized Difference Vegetation Index (NDVI) in Pasture Areas." In 2024 8th International Symposium on Instrumentation Systems, Circuits and Transducers (INSCIT). IEEE, 2024. http://dx.doi.org/10.1109/inscit62583.2024.10693407.
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Anggari, Ega Asti, Sigit Heru Murti, and Wahyudi Hasbi. "Implications Atmospheric Correction for Normalized Difference Vegetation Index (NDVI) Analysis in Multispectral Camera LAPAN A3." In 2024 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology (ICARES). IEEE, 2024. https://doi.org/10.1109/icares64249.2024.10768097.
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Silva, Hiago Quaresma da, Nataliel de Almeida Costa, José Alberto Tostes, and Felipe da Silva Duarte Lopes. "O impacto da perda arbórea no clima urbano da cidade de Macapá-AP." In XX ENCONTRO NACIONAL DE TECNOLOGIA DO AMBIENTE CONSTRUÍDO. UFAL, 2024. http://dx.doi.org/10.46421/entac.v20i1.6098.
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As mudanças climáticas no mundo estão cada vez mais em evidência em consequência do aumento nas temperaturas em zonas urbanas, o que acarreta elevado desconforto térmico com a formação das ilhas de calor. Pesquisas tem se voltado para compreender as variáveis envolvidas no fenômeno, com o intuito de diminuir e mitigar seus efeitos. No contexto amazônico, o ano de 2023 foi considerado um dos mais quentes da história, com altos índices de queimadas no estado do Amapá. Sendo assim, este trabalho busca fazer uma análise comparativa da relação de Normalize Difference Vegetation Index (NDVI) e Temperatura de Superfície da cidade de Macapá nos anos de 2013 e 2023. Para os mapas de NDVI foi utilizado dados do satélite Sentinel-2 utilizando a plataforma Google Earth Engine e para mapa de temperatura foi feita uma análise comparativa utilizando dados de satélite MODIS. Os resultados demonstram uma correlação entre a baixa densidade de vegetação e aumento da temperatura superficial, com destaque para os novos bairros do município. Os dados obtidos são primordiais para a construção de políticas públicas de desenvolvimento urbano sustentável e soluções baseadas na natureza.
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Nikolov, Asen, Vesselin Koutev, Olga Nitcheva, Donka Shopova, and Polya Dobreva. "CORRELATION OF REMOTE SENSING DATA (NDVI) WITH GROUND MEASURED DATA." In 24th SGEM International Multidisciplinary Scientific GeoConference 2024. STEF92 Technology, 2024. https://doi.org/10.5593/sgem2024/5.1/s20.08.
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The overall objective of using Normalized Digital Vegetation Index � NDVI is to improve the analysis of vegetation information with remote sensing data. Such estimates are often derived by correlating the NDVI values measured remotely with the ground measured values of some variables. In the conditions of the experiment carried out with zucchini the NDVI index was measured with a Trimble Green Seeker handheld sensor at the full growth. Applied fertilizers contain various nitrogen and phosphorus sources, including ammonium and nitrate, phosphates, and polyphosphates. The highest NDVI value (0.802) was obtained with ammonium nitrate from nitrogen treatments without phosphorus fertilization. The nitrogen source fertilizer with the highest NDVI on a polyphosphate background was KSC - 0.800. The same fertilizer performed best in an orthophosphate background, with an NDVI of 0.815. Best NDVI values were obtained on orthophosphate background. Obtained results are statistically proven. Stronger correlation coefficient exists between NDVI and Zucchini yield � 0.72. The overall goal of using Normalized Digital Vegetation Index � NDVI to improve the analysis of vegetation information with remote sensing data is successful.
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Hadi, Sinan Jasim, Mustafa Tombul, and Omar F. Althuwaynee. "Trend of normalized difference vegetation index (NDVI) over Turkey." In Remote Sensing for Agriculture, Ecosystems, and Hydrology, edited by Christopher M. Neale and Antonino Maltese. SPIE, 2018. http://dx.doi.org/10.1117/12.2502163.
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Amar, Haddad, Beldjazia Amina, Kadi Zahia, Redjaimia Lilia, and Rached-Kanouni Malika. "THE NORMALIZED DIFFERENCE VEGETATION INDEX AS AN INDICATOR OF DYNAMICS." In GEOLINKS Conference Proceedings. Saima Consult Ltd, 2021. http://dx.doi.org/10.32008/geolinks2021/b2/v3/27.
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Mediterranean ecosystems are considered particularly sensitive to climate change. Any change in climatic factors affects the structure and functioning of these ecosystems and has an influence on plant productivity. The main objective of this work is to characterize one of the Mediterranean ecosystems; the Chettaba forest massif (located in the North-East of Algeria) from a vegetation point of view and their link with monthly variations using Landsat 8 satellite images from five different dates (June 25, 2017, July 27, 2017, August 28, 2017, October 15, 2017). The comparison of NDVI values in Aleppo pine trees was performed using analysis of variance and the use of Friedman's non-parametric test. The Mann-Kendall statistical method was applied to the monthly distribution of NDVI values to detect any trends in the data over the study period. The statistical results of NDVI of Aleppo pine trees indicate that the maximum value is recorded in the month of June, while the lowest values are observed in the month of August where the species studied is exposed to periods of thermal stress.
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Dissanayake, D. M. D. O. K., and K. M. Kurugama. "Remote sensing and GIS approach to evaluate the UHI effect in Colombo city using landsat satellite data." In International Symposium on Earth Resources Management & Environment - ISERME 2023. Department of Earth Resources Engineering, 2023. http://dx.doi.org/10.31705/iserme.2023.13.
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This study examines Colombo’s heat island effect due to rapid development, with factors including urbanisation, reduced vegetation, increased energy use, heat-absorbing surfaces, and waste heat. Urban expansion absorbs and releases heat, raising night temperatures, while reduced vegetation disrupts natural temperature regulation. Energy consumption from air conditioning, industry, and transport worsens the effect. Heat-trapping surfaces and waste heat intensify the problem. The study analyses land surface temperature (LST), normalized vegetation difference index (NDVI), normalised difference building index (NDBI), and albedo’s role in the urban heat island (UHI) effect. UHI spread north, east, and southeast from 2001 to 2019. NDVI inversely correlates with LST, indicating vegetation mitigates UHI; NDBI positively correlates, showing that built areas contribute. Lower albedo values heighten UHI by absorbing more solar radiation. Urban thermal difference index (UTFVI) assessment identifies 27% of the region under high thermal stress. Future Colombo urban planning should integrate strategies like urban greening, cool roofs, sustainable planning, energy efficiency, and public awareness to address the UHI effect, enhance residents’ lives, and promote sustainability. Successful implementation requires collaboration among policymakers, urban planners, and residents for a resilient urban environment.
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De Ocampo, Anton Louise P. "Normalized Difference Vegetation Index (NDVI) Estimation based on Filter Augmented Imaging." In 2023 International Electrical Engineering Congress (iEECON). IEEE, 2023. http://dx.doi.org/10.1109/ieecon56657.2023.10126616.
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AlShamsi, Meera R. "Vegetation extraction from high-resolution satellite imagery using the Normalized Difference Vegetation Index (NDVI)." In SPIE Remote Sensing, edited by Lorenzo Bruzzone and Francesca Bovolo. SPIE, 2016. http://dx.doi.org/10.1117/12.2241768.
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Ratnayake, Ranitha. "Forest cover estimation using normalized difference vegetation index (NDVI) in plantation forest." In International Symposium on Remote Sensing, edited by Manfred Owe and Guido D'Urso. SPIE, 2002. http://dx.doi.org/10.1117/12.454191.
Full textReports on the topic "NDVI (Normalized Difference Vegetation Index)"
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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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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.), 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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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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Norris, Jodi, and Christopher Calvo. Trends in satellite-derived phenology in grasslands and shrublands of Southern Colorado Plateau Network parks. National Park Service, 2025. https://doi.org/10.36967/2312720.
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Purpose: The Southern Colorado Plateau Network (SCPN) used satellite-derived normalized difference vegetation index (NDVI) data over a 20-year period (2003–2022) to examine trends and patterns of variability in 12 grassland and shrubland study areas within SCPN park units. Methods: SCPN developed daily NDVI records for each study area, from which analyses and visualizations were derived. Raster graphs were created to facilitate data interpretation. Trends were calculated for each ranked day of the spring and monsoon seasons: For each study area, season, and year, each day was ranked from the highest NDVI value to the lowest. Total NDVI change was then estimated for the spring and monsoon season NDVI peaks (day-rank = 1) as well as all other non-peak day ranks for each season. The same analyses were also applied to subsets of pixels corresponding to more productive and less productive pixels within each study area. Results: The study areas had many “likely” trends in NDVI for ranked-daily values, with a smaller number of “very likely” and “highly likely” trends. Monsoon season trends were consistently negative or neutral for more northerly parks—Aztec Ruins National Monument (NM), Chaco Culture National Historical Park (NHP), and Glen Canyon National Recreation Area (NRA); and consistently positive or neutral for more southerly parks—Petrified Forest National Park (NP), Petroglyph NM, and Wupatki NM. Spring season trends varied spatially and included both positive and negative trends. Aztec Ruins NM and Glen Canyon NRA exhibited consistently negative or neutral trends for both seasons. Raster graphs revealed patterns including the variability in timing and intensity of seasonal greenness peaks and multi-year patterns. The raster graphs and associated data release can be used to evaluate the potential for the phenological patterns to correlate with on-the-ground conditions for wildlife resources that depend on vegetation. These data may also be used to parameterize models that relate climate to vegetation condition. Multi-year consecutive seasons of below-average NDVI were found in some records: At Wupatki NM, low NDVI from spring 2017 to spring 2021 corresponded in time with a widespread juniper dieback in 2021 in the adjacent pinyon-juniper woodlands of the area. Consecutive seasons of low NDVI also occurred in Petroglyph NM from 2008 to 2013 and at Aztec Ruins NM from 2020 to 2022. These records show the potential to link plant community stress to disturbance response thresholds, which is a subject of ongoing SCPN research.
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Spence, John, Ken Hyde, and Vanessa Glynn-Linaris. 1995–2017 analysis of vegetation change using NDVI data at Glen Canyon National Recreation Area: Focused condition assessment report. National Park Service, 2023. http://dx.doi.org/10.36967/2299497.
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This Focused Condition Assessment examines the impacts of the recent 2000–2020 long-term drought on the vegetation and soils of Glen Canyon National Recreation Area (GLCA). With support from the NASA DEVELOP Program, summer (June–August) Normalized Difference Vegetation Index (NDVI) values from 1995 to 2017 (excluding 2012 which was not available), measuring greenness and phenology in the vegetation, were analyzed for two periods. The first period from 1995–1999 included the pre-drought period, when precipitation was average to above average. Most years of the second period, 2000–2020, were drier than average as part of the severe drought that began in late 1999 and has continued to present (Lukas and Payton 2020). The NDVI values included mean values and were analyzed for 42 soil units, 20 associated NRCS Ecological Sites (ecosites), and the 10 most widespread vegetation alliances derived from the GLCA vegetation classification. Unvegetated rock outcrops, other exposed bedrock areas, and cliffs, which are extensive in GLCA, were not included. With the exception of some riparian areas, mean NDVI values for all upland soils, ecosites and alliances declined from pre-drought conditions. The areas showing the largest declines were clay soils, shallow sandy loam and other shallow soils and associated ecosites and alliances. Talus vegetation and mid- to upper elevation pinyon-juniper (Pinus edulis-Juniperus osteosperma) woodlands showed the smallest declines. Deeper sandy and sandy loam sites showed intermediate declines. Particularly large declines occurred in shallow soil arid sites dominated by shadscale (Atriplex confertifolia) and other saltbush species. Blackbrush (Coleogyne ramosissima), one of the dominant species in the park, showed moderate declines, primarily on shallower soils. No evidence for widespread death in either blackbrush or pinyon-juniper woodlands were noted, although recent severe drought and a weakened Arizona Monsoon since 2018 may be causing impacts to the woodland species. Relationships with livestock grazing are also examined, based on data collected on long-term monitoring plots established between 2008 and 2020. There is evidence that areas with intensive livestock grazing have shown larger declines than ungrazed areas, but these impacts need to be explored more fully at the local allotment and pasture level, and correlated with actual grazing animal unit months (AUM)’s. Several management recommendations are made, including additional plot-based long-term monitoring, exploration of cultural resource inventories and erodible soils, how these observed changes can affect livestock grazing management decisions in the park, and further exploration using NDVI data from 2018 and forward.
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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.
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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.
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Anderson, Gerald L., and Kalman Peleg. Precision Cropping by Remotely Sensed Prorotype Plots and Calibration in the Complex Domain. United States Department of Agriculture, 2002. http://dx.doi.org/10.32747/2002.7585193.bard.
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This research report describes a methodology whereby multi-spectral and hyperspectral imagery from remote sensing, is used for deriving predicted field maps of selected plant growth attributes which are required for precision cropping. A major task in precision cropping is to establish areas of the field that differ from the rest of the field and share a common characteristic. Yield distribution f maps can be prepared by yield monitors, which are available for some harvester types. Other field attributes of interest in precision cropping, e.g. soil properties, leaf Nitrate, biomass etc. are obtained by manual sampling of the filed in a grid pattern. Maps of various field attributes are then prepared from these samples by the "Inverse Distance" interpolation method or by Kriging. An improved interpolation method was developed which is based on minimizing the overall curvature of the resulting map. Such maps are the ground truth reference, used for training the algorithm that generates the predicted field maps from remote sensing imagery. Both the reference and the predicted maps are stratified into "Prototype Plots", e.g. 15xl5 blocks of 2m pixels whereby the block size is 30x30m. This averaging reduces the datasets to manageable size and significantly improves the typically poor repeatability of remote sensing imaging systems. In the first two years of the project we used the Normalized Difference Vegetation Index (NDVI), for generating predicted yield maps of sugar beets and com. The NDVI was computed from image cubes of three spectral bands, generated by an optically filtered three camera video imaging system. A two dimensional FFT based regression model Y=f(X), was used wherein Y was the reference map and X=NDVI was the predictor. The FFT regression method applies the "Wavelet Based", "Pixel Block" and "Image Rotation" transforms to the reference and remote images, prior to the Fast - Fourier Transform (FFT) Regression method with the "Phase Lock" option. A complex domain based map Yfft is derived by least squares minimization between the amplitude matrices of X and Y, via the 2D FFT. For one time predictions, the phase matrix of Y is combined with the amplitude matrix ofYfft, whereby an improved predicted map Yplock is formed. Usually, the residuals of Y plock versus Y are about half of the values of Yfft versus Y. For long term predictions, the phase matrix of a "field mask" is combined with the amplitude matrices of the reference image Y and the predicted image Yfft. The field mask is a binary image of a pre-selected region of interest in X and Y. The resultant maps Ypref and Ypred aremodified versions of Y and Yfft respectively. The residuals of Ypred versus Ypref are even lower than the residuals of Yplock versus Y. The maps, Ypref and Ypred represent a close consensus of two independent imaging methods which "view" the same target. In the last two years of the project our remote sensing capability was expanded by addition of a CASI II airborne hyperspectral imaging system and an ASD hyperspectral radiometer. Unfortunately, the cross-noice and poor repeatability problem we had in multi-spectral imaging was exasperated in hyperspectral imaging. We have been able to overcome this problem by over-flying each field twice in rapid succession and developing the Repeatability Index (RI). The RI quantifies the repeatability of each spectral band in the hyperspectral image cube. Thereby, it is possible to select the bands of higher repeatability for inclusion in the prediction model while bands of low repeatability are excluded. Further segregation of high and low repeatability bands takes place in the prediction model algorithm, which is based on a combination of a "Genetic Algorithm" and Partial Least Squares", (PLS-GA). In summary, modus operandi was developed, for deriving important plant growth attribute maps (yield, leaf nitrate, biomass and sugar percent in beets), from remote sensing imagery, with sufficient accuracy for precision cropping applications. This achievement is remarkable, given the inherently high cross-noice between the reference and remote imagery as well as the highly non-repeatable nature of remote sensing systems. The above methodologies may be readily adopted by commercial companies, which specialize in proving remotely sensed data to farmers.
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Siripong, Absornsuda. The linkage of EI Nino and La Nina on the environment and resources at Ban Don Bay and Surat Thani : final research report. Chulalongkorn University, 2007. https://doi.org/10.58837/chula.res.2007.38.
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The Landsat data in 3 periods: which are during the El Nino years (1993, 1994,1997 and 2002); the La Nina years (1988, 1998 and 2000); and Normal year (1989, 1996 and 1999), are classified for landuse types and by NDVI (Normalised Difference Vegatation Index), We found that during El Nino years, the rainfall anomalies are minus (less than normal), the air temperature anomalies are plus (hotter than normal), Tapi river dischange anomalies are minus, and NDVI’s are lower than normal (less growing vegetations). During La Nina years, the rainfall anomalies are plus (higher than normal), the air temperature anomalies are minus (colder than normal), the Tapi river dischange anomalies are plus, and NDVI’s are higher than normal (much growing vegetations). In 1998, first half of the year was El Nino, and last half of the year was La Nina. However, we used the data on 27 April 1998, which is in the El Nino period, so it received the impact of El Nino. For fisheries statistics at Surat Thani during El Nino years, the quantity of fish catch and the yields of aquacultures (shrimp and fish) were higher than average. During the La Nina years the same parameters were lower than average the areas of shrimp ponds have been increased with the increasing population. The population of Surat Thani has been increased every year. During the El Nino years, the areas of natural and mangroves were decreased, while during the El Nina years, the areas were increased. El Nino caused higher frequency of tropical cyclone. La Nina also caused tropical cyclone but lesser than El Nino linkage. The linkage of ENSO on MSL is not very clear owing to the small quantity of data. During La Nina years, MSLs show plus and minus anomalies for one each year. However, we may conclude that El Nino causes higher MLS than mean value. El Nino caused lower river runoff than normal. La Nina caused higher runoff than normal.
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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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