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1
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.
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Bala, R., R. Prasad, V. P. Yadav, and J. Sharma. "A COMPARATIVE STUDY OF LAND SURFACE TEMPERATURE WITH DIFFERENT INDICES ON HETEROGENEOUS LAND COVER USING LANDSAT 8 DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-5 (November 19, 2018): 389–94. http://dx.doi.org/10.5194/isprs-archives-xlii-5-389-2018.
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<p><strong>Abstract.</strong> The temperature rise in urban areas has become a major environmental concern. Hence, the study of Land surface temperature (LST) in urban areas is important to understand the behaviour of different land covers on temperature. Relation of LST with different indices is required to study LST in urban areas using satellite data. The present study focuses on the relation of LST with the selected indices based on different land cover using Landsat 8 OLI (Operational Land Imager) and TIRS (Thermal Infrared Sensor) data in Varanasi, India. A regression analysis was done between LST and Normalized Difference Vegetation index (NDVI), Normalized Difference Soil Index (NDSI), Normalized Difference Built-up Index (NDBI) and Normalized Difference Water Index (NDWI). The non-linear relations of LST with NDVI and NDWI were observed, whereas NDBI and NDSI were found to show positive linear relation with LST. The correlation of LST with NDSI was found better than NDBI. Further analysis was done by choosing 25 pure pixels from each land cover of water, vegetation, bare soil and urban areas to determine the behaviour of indices on LST for each land cover. The investigation shows that NDSI and NDBI can be effectively used for study of LST in urban areas. However, NDBI can explain urban LST in the better way for the regions without water body.</p>
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KAMBLE, M. V., K. GHOSH, M. RAJEEVAN, and R. P. SAMUI. "Drought monitoring over India through Normalized Difference Vegetation Index (NDVI)." MAUSAM 61, no. 4 (2021): 537–46. http://dx.doi.org/10.54302/mausam.v61i4.911.
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Normalized Difference Vegetation Index (NDVI) is a simple index to monitor the state of vegetation (stressed/unstressed) which can be derived from satellite data. Hence an attempt is made to find out the vegetation responses to rainfall through NDVI over the study area. Applicability of NDVI in drought monitoring is discussed using the NDVI and rainfall data for the period 1982-2003. The anomaly of NDVI is compared with the percentage departure of rainfall of corresponding years. Results showed a significant relation between the NDVI with the percentage departure of rainfall. The time series plots of averaged NDVI and seasonal rainfall (June-September) are done for NW India (21° N - 31° N, 68° E - 78° E), Central India (22° N - 27° N, 70° E - 77° E) and Peninsular India (16° N - 21° N, 74° E - 79° E) over the period 1982-2003 to analyze changes in vegetation pattern of India during the last two decades. Results indicated a clear linear relationship over NW and Central India. NDVI anomalies and the corresponding cumulative rainfall showed significantly linear correlation of 0.69 over NW India and 0.57 over Central India significant at 1% level but the correlation is found to be insignificant over Peninsular India which was only 0.04. Trend analysis of averaged NDVI over India showed that during last two decades the vegetation status had quite improved over the dry farming tracts of India.
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Табунщик, В. А., Р. В. Горбунов та А. А. Даниленко. "Оценка вегетационного индекса NDVI на территории города федерального значения Севастополь в 2017 году по результатам анализа космических снимков Sentinel-2". Труды Карадагской научной станции им. Т.И. Вяземского - природного заповедника РАН, № 4 (12) (19 квітня 2021): 56–70. http://dx.doi.org/10.21072/eco.2021.12.03.
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Цель исследования – изучить пространственное распределение значений вегетационного индекса Normalized Difference Vegetation Index (NDVI) и выявить слабоиспользуемые земли в сельском хозяйстве на территории города федерального значения Севастополь в 2017 году. Для расчетов вегетационного индекса Normalized Difference Vegetation Index (NDVI) были использованы космические снимки Sentinel-2 с минимальными показателями облачности за период с 16 февраля 2017 года по 29 октября 2017 года. Космические снимки были предварительно обработаны и прошли атмосферную коррекцию. Результаты исследования показывают, что на территории города федерального значения Севастополь в 2017 году средние значения вегетационного индекса Normalized Difference Vegetation Index (NDVI) колеблются достигают 0,61. Выявлены семь участков сельскохозяйственных земель со средними значениями вегетационного индекса Normalized Difference Vegetation Index (NDVI) менее 0,2, что свидетельствует о слабом развитии растительности и вовлечении в сельскохозяйственную деятельность.
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Priya, M. V., R. Kalpana, S. Pazhanivelan, et al. "Monitoring vegetation dynamics using multi-temporal Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) images of Tamil Nadu." Journal of Applied and Natural Science 15, no. 3 (2023): 1170–77. http://dx.doi.org/10.31018/jans.v15i3.4803.
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Vegetation indices serve as an essential tool in monitoring variations in vegetation. The vegetation indices used often, viz., normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) were computed from MODIS vegetation index products. The present study aimed to monitor vegetation's seasonal dynamics by using time series NDVI and EVI indices in Tamil Nadu from 2011 to 2021. Two products characterize the global range of vegetation states and processes more effectively. The data sources were processed and the values of NDVI and EVI were extracted using ArcGIS software. There was a significant difference in vegetation intensity and status of vegetation over time, with NDVI having a larger value than EVI, indicating that biomass intensity varies over time in Tamil Nadu. Among the land cover classes, the deciduous forest showed the highest mean values for NDVI (0.83) and EVI (0.38), followed by cropland mean values of NDVI (0.71) and EVI (0.31) and the lowest NDVI (0.68) and EVI (0.29) was recorded in the scrubland. The study demonstrated that vegetation indices extracted from MODIS offered valuable information on vegetation status and condition at a short temporal time period.
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Piekarski, Paweł, and Zbigniew Zwoliński. "Temporal Variation in Vegetation Indexes for Pine and Beech Stands During the Vegetation Season, Szczecin Lowland, Poland." Quaestiones Geographicae 33, no. 3 (2014): 131–43. http://dx.doi.org/10.2478/quageo-2014-0037.
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Abstract Located in north-western Poland, the Bukowska Forest and Goleniowska Forest are vast woodlands consisting of areas with a homogeneous species composition that have been scarcely affected by humans. In this respect, they provided an excellent subject for scientific research, the purpose of which was to determine quantitative differences in selected vegetation indices of pine and beech stands in various periods during their vegetation seasons. Another purpose was to characterize the variation in these indices for each stand in its vegetation season. Four Landsat 5 TM images taken in 2007 and 2010 at four different points of vegetation season provided the basis for the analysis. In the analysis, 19 wooded areas with a homogeneous species composition were tested. In Bukowska Forest, the tested area was a beech stand, and in Goleniowska Forest, it was a pine stand. Acquired data was used to calculate the following vegetation indices: Normalized Difference Vegetation Index (NDVI), Transformed Vegetation Index (TVI), Green Normalized Difference Vegetation Index (Green NDVI), Normalized Difference Greenness Index (NDGI) and Normalized Difference Index (NDI). Subsequent research allowed to establish that the beech and pine stands differed significantly with respect to their calculated vegetation indices. These differences derived both from the biochemical and structural attributes of leaves and needles, as well as from transformations that occur in the stands during vegetation seasons. Analysis of the indices’ allowed us to determine these differences and the influence of the stands’ phenological phases on the indices.
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Gerardo, Romeu, and Isabel P. de Lima. "Monitoring Duckweeds (Lemna minor) in Small Rivers Using Sentinel-2 Satellite Imagery: Application of Vegetation and Water Indices to the Lis River (Portugal)." Water 14, no. 15 (2022): 2284. http://dx.doi.org/10.3390/w14152284.
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Duckweed species, particularly Lemna minor, are widely found in freshwaters all over the world. This macrophyte provides multiple ecosystems’ functions and services, but its excessive proliferation can have negative environmental impacts (including ecological and socio-economic impacts). This work explores the use of remote sensing tools for mapping the dynamics of Lemna minor in open watercourses, which could contribute to identifying suitable monitoring programs and integrated management practices. The study focuses on a selected section of the Lis River (Portugal), a small river that is often affected by water pollution. The study approach uses spatiotemporal multispectral data from the Sentinel-2 satellite and from 2021 and investigates the potential of remote sensing-based vegetation and water indices (Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Normalized Difference Aquatic Vegetation Index (NDAVI), Green Red Vegetation Index (GRVI), Normalized Difference Water Index (NDWI)) for detecting duckweeds’ infestation and its severity. The NDAVI was identified as the vegetation index (VI) that better depicted the presence of duckweeds in the surface of the water course; however, results obtained for the other VIs are also encouraging, with NDVI showing a response that is very similar to NDAVI. Results are promising regarding the ability of remote sensing products to provide insight into the behavior of Lemna minor and to identify problematic sections along small watercourses.
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Muhsin, Israa J. "Assessment of vegetal cover changes using Normalized Difference Vegetation Index (NDVI) and subtractive (NDVI) time-series, Karbala province, Iraq." Iraqi Journal of Physics (IJP) 15, no. 35 (2018): 133–41. http://dx.doi.org/10.30723/ijp.v15i35.62.
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Karbala province regarded one part significant zones in Iraq and considered an economic resource of vegetation such as trees of fruits, sieve and other vegetation. This research aimed to utilize Normalized Difference Vegetation index (NDVI) and Subtracted (NDVI) for investigating the current vegetation cover at last four decay. The Normalized Difference Vegetation Index (NDVI) is the most extensively used satellite index of vegetation health and density. The primary goals of this research are gather a gathering of studied area (Karbala province) satellite images in sequence time for a similar region, these image captured by Landsat (TM 1985, TM 1995, ETM+ 2005 and Landsat 8 OLI (Operational Land Imager) 2015. Preprocessing such gap filling consider being vital stride has been implied on the defected image which captured in Landsat 2005 and isolate the regions of studied region. The Assessment vegetal cover changes of the studied area in this paper has been implemented using Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI) and change detection techniques such as Subtracted (NDVI) method also have been used to detect the change in vegetal cover of the studied region. Many histogram and statistical properties were illustrated has been computed. From The results shows there are increasing in the vegetal cover from 1985 to 2015.
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Anak Kemarau, Ricky, and Oliver Valentine Eboy. "Application of Remote Sensing on El Niño Extreme Effect in Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI)." Malaysian Journal of Applied Sciences 6, no. 1 (2021): 46–56. http://dx.doi.org/10.37231/myjas.2021.6.1.277.
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The years 1997/1998 and 2015/2016 saw the worst El Niño occurrence in human history. The occurrence of El Niño causes extreme temperature events which are higher than usual, drought and prolonged drought. The incident caused a decline in the ability of plants in carrying out the process of photosynthesis. This causes the carbon dioxide content to be higher than normal. Studies on the effects of El Niño and its degree of strength are still under-studied especially by researchers in the tropics. This study uses remote sensing technology that can provide spatial information. The first step of remote sensing data needs to go through the pre-process before building the NDVI (Normalized Difference Vegetation Index) and Normalized Difference Water Index (NDWI) maps. Next this study will identify the relationship between Oceanic Nino Index (ONI) with Application Remote Sensing in The Study Of El Niño Extreme Effect 1997/1998 and 2015/2016 On Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI)NDWI and NDWI landscape indices. Next will make a comparison, statistical and spatial information space between NDWI and NDVI for each year 1997/1998 and 2015/2016. This study is very important in providing spatial information to those responsible in preparing measures in reducing the impact of El Niño.
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Barman, Pritam Kumar, Shivani Rawat, Avni Kumari, and Afaq Majid Wani. "Monitoring the Vegetation Condition of Gorumara National Park Using NDVI and NDMI Indices." International Journal of Bio-resource and Stress Management 15, Feb, 2 (2024): 01–07. http://dx.doi.org/10.23910/1.2024.5052.
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The present study was conducted from November, 2022 to June, 2023 aims to analyze and detect changes in vegetation using the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Moisture Index (NDMI) in Gorumara National Park, Jalpaiguri district, West Bengal, India. To calculate NDVI and NDMI values, Landsat 8 level-1 images acquired between 2016 and 2021. Different band combinations of the remote sensing data are analyzed to classify the vegetation condition and cover. For this study, the 4 (Red), 5 (NIR), and 6 (SWIR) multi-spectral band combinations are used separately. The rising use of satellite remote sensing and Geographic Information System (GIS) for civilian purposes has shown itself to be the most cost-effective and time-effective method of mapping and monitoring vegetation conditions and changes. Open-source software such as QGIS and the Semi-Automatic Classification Plugin (SCP) was used for mapping and image pre-processing. According to the NDVI and NDMI classifications, the area under high vegetation and high moisture content has slightly increased by 0.15% and 0.23%, respectively. During the study period the high vegetation and very high moisture content areas covered most areas in 2020 and 2017, respectively. According to the findings, the NDVI and NDMI are very helpful in identifying the area’s surface features, which is very helpful for determining the vegetation’s general health, providing the required data for long-term conservation efforts, and developing efficient management plans.
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Daliman, Shaparas, Mary Jane Anak Michael, Pradnya Paramarta Raditya Rendra, Emi Sukiyah, Mohamad Sapari Dwi Hadian, and Nana Sulaksana. "Dual Vegetation Index Analysis and Spatial Assessment in Kota Bharu, Kelantan using GIS and Remote Sensing." BIO Web of Conferences 131 (2024): 05009. http://dx.doi.org/10.1051/bioconf/202413105009.
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Vegetation serves as an indicator of environmental conditions in ecological classifications. In addition, vegetation index analysis can also benefit farmers and agricultural planners by optimising crop selection and irrigation practices. The spatial distribution of healthy vegetation can increase agricultural productivity. This study focuses on the Kota Bharu district in the state of Kelantan, Malaysia that aims to recognise the vegetation indices Normalized Difference Vegetation Index (NDVI) and Green Normalized Difference Vegetation Index (GNDVI). NDVI analysis measures reflected visible and near-infrared light to identify and evaluate living green plants. The Green Normalized Difference Vegetation Index (GNDVI) has a higher saturation threshold and is more sensitive to plant chlorophyll levels than NDVI. This approach works in agricultural environments with dense canopies or advanced crop development. The average accuracy level for NDVI 2023 is 78% while the average accuracy level for GNDVI 2023 is 76%. The value of kappa coefficient for NDVI and GNDVI for 2023 respectively are 0.73 and 0.72 which considered to be acceptable and represents the good correspondence.
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Wilson, Natalie R., and Laura M. Norman. "Analysis of vegetation recovery surrounding a restored wetland using the normalized difference infrared index (NDII) and normalized difference vegetation index (NDVI)." International Journal of Remote Sensing 39, no. 10 (2018): 3243–74. http://dx.doi.org/10.1080/01431161.2018.1437297.
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Fang, Wenjing, Hongfen Zhu, Shuai Li, Haoxi Ding, and Rutian Bi. "Rapid Identification of Main Vegetation Types in the Lingkong Mountain Nature Reserve Based on Multi-Temporal Modified Vegetation Indices." Sensors 23, no. 2 (2023): 659. http://dx.doi.org/10.3390/s23020659.
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Nature reserves are among the most bio-diverse regions worldwide, and rapid and accurate identification is a requisite for their management. Based on the multi-temporal Sentinel-2 dataset, this study presents three multi-temporal modified vegetation indices (the multi-temporal modified normalized difference Quercus wutaishanica index (MTM-NDQI), the multi-temporal modified difference scrub grass index (MTM-DSI), and the multi-temporal modified ratio shaw index (MTM-RSI)) to improve the classification accuracy of the remote sensing of vegetation in the Lingkong Mountain Nature Reserve of China (LMNR). These three indices integrate the advantages of both the typical vegetation indices and the multi-temporal remote sensing data. By using the proposed indices with a uni-temporal modified vegetation index (the uni-temporal modified difference pine-oak mixed forest index (UTM-DMI)) and typical vegetation indices (e.g., the ratio vegetation index (RVI), the difference vegetation index (DVI), and the normalized difference vegetation index (NDVI)), an optimal feature set is obtained that includes the NDVI of December, the NDVI of April, and the UTM-DMI, MTM-NDQI, MTM-DSI, and MTM-RSI. The overall accuracy (OA) of the random forest classification (98.41%) and Kappa coefficient of the optimal feature set (0.98) were higher than those of the time series NDVI (OA = 96.03%, Kappa = 0.95), the time series RVI (OA = 95.56%, Kappa = 0.95), and the time series DVI (OA = 91.27%, Kappa = 0.90). The OAs of the rapid classification and the Kappa coefficient of the knowledge decision tree based on the optimal feature set were 95.56% and 0.95, respectively. Meanwhile, only three of the seven vegetation types were omitted or misclassified slightly. Overall, the proposed vegetation indices have advantages in identifying the vegetation types in protected areas.
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Suhardono, Sapta, Iva Yenis Septiariva, Sovia Wijayanti, Naila Maulida Ibriza, and I. Wayan Koko Suryawan. "Spatial and temporal monitoring of drought hazards in Opak Watershed." E3S Web of Conferences 605 (2025): 03041. https://doi.org/10.1051/e3sconf/202560503041.
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The Opak Watershed in Yogyakarta Province has been under persistent drought threat. This study monitored drought conditions spatially and temporally using Sentinel-2 satellite imagery and rainfall data. The Standardized Precipitation Index (SPI) was employed for drought assessment based on rainfall, while the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Moisture Index (NDMI) evaluated drought via satellite images. A Pearson correlation analysis revealed significant relationships: SPI and NDVI (r = 0.527), SPI and NDMI (r = 0.704), and NDVI and NDMI (r = 0.921). The findings provide a comprehensive understanding of drought patterns and trends, revealing critical insights into the development of drought conditions over time and across various locations within the Opak Watershed.
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Gonzalez Betancourt, Mauricio, and Zaira Liceth Mayorga-Ruíz. "Normalized difference vegetation index for rice management in El Espinal, Colombia." DYNA 85, no. 205 (2018): 47–56. http://dx.doi.org/10.15446/dyna.v85n205.69516.
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Se evaluaron las imágenes aéreas y el NDVI como herramientas para la supervisión del arroz a gran escala. El índice de vegetación de diferencia normalizada (NDVI) se utilizó para identificar problemas en el desarrollo de la variedad de arroz FEDEARROZ-2000, la cual es resistente al virus de la hoja blanca y al daño directo de la "sogata". Se estimó la dinámica temporal del NDVI para FEDEARROZ-2000. En la Etapa de Desarrollo de la Panícula del Arroz (EDPA), el NDVI inferior a 0,8 se relacionó con áreas con problemas de nivelación, estrés hídrico y diferencias en el estado de las plantas. El NDVI de la EDPA tuvo una correlación positiva significativa con las panículas/m2, el peso de los 1000 granos, y con el rendimiento (Coeficiente de correlación de Pearson R≥0.86; Probabilidad≤0.04). El NDVI en la etapa lechosa ayudó a identificar ambientes de producción y a programar áreas para la cosecha.
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Justino, Sérvio Túlio Pereira, Rafael Barroca Silva, Iraê Amaral Guerrini, Richardson Barbosa Gomes da Silva, and Danilo Simões. "Monitoring Environmental Degradation and Spatial Changes in Vegetation and Water Resources in the Brazilian Pantanal." Sustainability 17, no. 1 (2024): 51. https://doi.org/10.3390/su17010051.
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Diagnosing climate variability and environmental change in floodable regions is essential for understanding and mitigating impacts on natural ecosystems. Our objective was to characterize environmental degradation in the Brazilian Pantanal by identifying changes in vegetation and water cover over a 30-year period using remote sensing techniques. We evaluated surface physical–hydric parameters, including Land Use and Land Cover (LULC) maps, Normalized Difference Vegetation Index (NDVI), Modified Normalized Difference Water Index (MNDWI), Normalized Difference Moisture Index (NDMI), and precipitation data. There was a decrease in the area of water bodies (−9.9%), wetlands (−5.7%), and forest formation (−3.0%), accompanied by an increase in the area of pastureland (7.4%). The NDVI showed significant changes in vegetation cover (−0.69 to 0.81), while the MNDWI showed a decrease in water surface areas (−0.73 to 0.93) and the NDMI showed a continuous decrease in vegetation moisture (−0.53 to 1). Precipitation also decreased over the years, reaching a minimum of 595 mm. Vegetation indices and land use maps revealed significant changes in vegetation and loss of water bodies in the Pantanal, reinforcing the need for sustainable management, recovery of degraded areas, and promotion of ecotourism to balance environmental conservation and local development.
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Nádudvari, Ádám. "The localization of urban heat island in the Katowice conurbation (Poland) using the combination of land surface temperature, Normalized Difference Vegetation Index and Normalized Difference Built-up Index." Geographia Polonica 94, no. 1 (2021): 111–29. http://dx.doi.org/10.7163/gpol.0196.
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The localization of Surface Urban Heat Island (SUHI) as a potential heat risk for the urban population was evaluated. The paper aimed to propose an approach to quantify and localize (SUHI) based on Landsat series TM, ETM+, OLI satellite imageries from the period 1996-2018 and recognize the Atmospheric Urban Heat Island (AUHI) effects from long term temperature measurements. Using the theoretical relation between the Normalized Difference Built-up Index (NDBI), the Normalized Difference Vegetation Index (NDVI) and the LST (Land Surface Temperature), SUHIintensity and SUHIrisk maps were created from the combination of LST, NDVI, NDBI using threshold values to localize urban heat island in the Katowice conurbation. Negative values of SUHI intensity characterize areas where there is no vegetation, highly built-up areas, and areas with high surface temperatures. The urban grow – revealed from SUHI – and global climate change are acting together to strengthen the global AUHI effect in the region as the temperature measurements were indicated.
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Naif, Salwa S., Dalia A. Mahmood, and Monim H. Al-Jiboori. "Seasonal normalized difference vegetation index responses to air temperature and precipitation in Baghdad." Open Agriculture 5, no. 1 (2020): 631–37. http://dx.doi.org/10.1515/opag-2020-0065.
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AbstractThe spatial distribution of urban vegetation cover is strongly related to climatological conditions, which play a vital role in urban cooling via shading and reducing ground surface temperature and effective strategy in mitigation urban heat island. Based on the Landsat satellite images, the quantitative normalized difference vegetation index (NDVI) was spatially mapped at two times for each year during 2008, 2013, 2019 in Baghdad. The NDVI values ranged from −1 to +1 with considering values larger than 0.2 indicate the dense healthy vegetation. In this study, the fractional areas of NDVI >0.2 were computed with their percentage. The responses of the NDVI during the growing seasons to two climate indices (i.e., air temperature and precipitation) were investigated. These climatic data obtained from the Iraqi Meteorological Organization and Seismology for the aforementioned years were used to explore the potential correlations between seasonal NDVI and above climate variables. The result shows that NDVI-derived vegetation growth patterns were highly correlated with their recording during the current growth seasons.
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Zhao, Qi, and Yonghua Qu. "The Retrieval of Ground NDVI (Normalized Difference Vegetation Index) Data Consistent with Remote-Sensing Observations." Remote Sensing 16, no. 7 (2024): 1212. http://dx.doi.org/10.3390/rs16071212.
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The Normalized Difference Vegetation Index (NDVI) is widely used for monitoring vegetation status, as accurate and reliable NDVI time series are crucial for understanding the relationship between environmental conditions, vegetation health, and productivity. Ground digital cameras have been recognized as important potential data sources for validating remote-sensing NDVI products. However, differences in the spectral characteristics and imaging methods between sensors onboard satellites and ground digital cameras hinder direct consistency analyses, thereby limiting the quantitative application of camera-based observations. To address this limitation and meet the needs of vegetation monitoring research and remote-sensing NDVI validation, this study implements a novel NDVI camera. The proposed camera incorporates narrowband dual-pass filters designed to precisely separate red and near-infrared (NIR) spectral bands, which are aligned with the configuration of sensors onboard satellites. Through software-controlled imaging parameters, the camera captures the real radiance of vegetation reflection, ensuring the acquisition of accurate NDVI values while preserving the evolving trends of the vegetation status. The performance of this NDVI camera was evaluated using a hyperspectral spectrometer in the Hulunbuir Grassland over a period of 93 days. The results demonstrate distinct seasonal characteristics in the camera-derived NDVI time series using the Green Chromatic Coordinate (GCC) index. Moreover, in comparison to the GCC index, the camera’s NDVI values exhibit greater consistency with those obtained from the hyperspectral spectrometer, with a mean deviation of 0.04, and a relative root mean square error of 9.68%. This indicates that the narrowband NDVI, compared to traditional color indices like the GCC index, has a stronger ability to accurately capture vegetation changes. Cross-validation using the NDVI results from the camera and the PlanetScope satellite further confirms the potential of the camera-derived NDVI data for consistency analyses with remote sensing-based NDVI products, thus highlighting the potential of camera observations for quantitative applications The research findings emphasize that the novel NDVI camera, based on a narrowband spectral design, not only enables the acquisition of real vegetation index (VI) values but also facilitates the direct validation of vegetation remote-sensing NDVI products.
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Naik, B. Chandrababu, and B. Anuradha. "Extraction of Water-body Area from High-resolution Landsat Imagery." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 6 (2018): 4111–19. https://doi.org/10.11591/ijece.v8i6.pp4111-4119.
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Extraction of water bodies from satellite imagery has been broadly explored in the current decade. So many techniques were involved in detecting of the surface water bodies from satellite data. To detect and extracting of surface water body changes in Nagarjuna Sagar Reservoir, Andhra Pradesh from the period 1989 to 2017, were calculated using Landsat-5 TM, and Landsat-8 OLI data. Unsupervised classification and spectral water indexing methods, including the Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI), Normalized Difference Water Index (NDWI), and Modified Normalized Difference Water Index (MNDWI), were used to detect and extraction of the surface water body from satellite data. Instead of all index methods, the MNDWI was performed better results. The Reservoir water area was extracted using spectral water indexing methods (NDVI, NDWI, MNDWI, and NDMI) in 1989, 1997, 2007, and 2017. The shoreline shrunk in the twenty-eight-year duration of images. The Reservoir Nagarjuna Sagar lost nearly around one-fourth of its surface water area compared to 1989. However, the Reservoir has a critical position in recent years due to changes in surface water and getting higher mud and sand. Maximum water surface area of the Reservoir will lose if such decreasing tendency follows continuously.
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Aini Rahmi, Muthi’ah, Parikesit Parikesit, and Susanti Withaningsih. "Vegetation change analysis using Normalized Difference Vegetation Index (NDVI) in Sumedang Regency." E3S Web of Conferences 495 (2024): 02007. http://dx.doi.org/10.1051/e3sconf/202449502007.
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Vegetation is a crucial element of livable and healthy cities and has been linked to a number of advantages, such as enhanced human health, habitat provision, and natural system regulation. Planning sustainable cities requires an understanding of and documentation of changes in urban vegetation. The Normalized Difference Vegetation Index (NDVI)'s spatial variance and driving force are useful for managing natural resources and protecting ecological environments. Using Sumedang Regency as the research area, the normalized vegetation index (NDVI) was computed using Landsat-7 ETM and Landsat 8 OLI/TIRS data from 2003 to 2023. The findings show that the high greenness index first declined and subsequently increased between 2003 and 2023. Sumedang Regency's high greenness index shrank in area between 2003 and 2018. In 2003, 144793.17 ha was categorised as high greenness index, but in 2018 the high greenness index was only 122392.08 ha. Furthermore, the index with non-vegetated land increases every year. This shows that Sumedang Regency continues to experience land use change into non-vegetated areas, such as settlements and bare land. This research can provide assistance for the development of a sustainable natural environment in Sumedang Regency.
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Song, Bonggeun, and Kyunghun Park. "Detection of Aquatic Plants Using Multispectral UAV Imagery and Vegetation Index." Remote Sensing 12, no. 3 (2020): 387. http://dx.doi.org/10.3390/rs12030387.
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In this study, aquatic plants in a small reservoir were detected using multispectral UAV (Unmanned Aerial Vehicle) imagery and various vegetation indices. A Firefly UAV, which has both fixed-wing and rotary-wing flight modes, was flown over the study site four times. A RedEdge camera was mounted on the UAV to acquire multispectral images. These images were used to analyze the NDVI (Normalized Difference Vegetation Index), ENDVI (Enhance Normalized Difference Vegetation Index), NDREI (Normalized Difference RedEdge Index), NGRDI (Normalized Green-Red Difference Index), and GNDVI (Green Normalized Difference Vegetation Index). As for multispectral characteristics, waterside plants showed the highest reflectance in Rnir, while floating plants had a higher reflectance in Rre. During the hottest season (on 25 June), the vegetation indices were the highest, and the habitat expanded near the edge of the reservoir. Among the vegetation indices, NDVI was the highest and NGRDI was the lowest. In particular, NGRDI had a higher value on the water surface and was not useful for detecting aquatic plants. NDVI and GNDVI, which showed the clearest difference between aquatic plants and water surface, were determined to be the most effective vegetation indices for detecting aquatic plants. Accordingly, the vegetation indices using multispectral UAV imagery turned out to be effective for detecting aquatic plants. A further study will be accompanied by a field survey in order to acquire and analyze more accurate imagery information.
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Fletcher, Reginald S., David E. Escobar, and Mani Skaria. "Evaluating Airborne Normalized Difference Vegetation Index Imagery for Citrus Orchard Surveys." HortTechnology 14, no. 1 (2004): 91–94. http://dx.doi.org/10.21273/horttech.14.1.0091.
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The normalized difference vegetation index (NDVI) provides relative estimates of vegetation vigor, density, and health. Little information is available on the application of NDVI imagery for citriculture. The objective of this study was to evaluate airborne NDVI imagery for assessing tree conditions in citrus (Citrus spp.) orchards. Images of two south Texas citrus groves with stressed and nonstressed trees were qualitatively evaluated. Stressed trees were easily detected from nonstressed trees in the images. The images were also helpful for developing survey plans of the citrus groves. Our results indicated that airborne NDVI images could be used as a tool to assess tree conditions in citrus orchards. Findings should be of interest to citrus growers, extension agents, agricultural consultants, and private surveying companies.
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Rahmat, Ali, Mustofa Abi Hamid, Muhammad Khoiru Zaki, and Abdul Mutolib. "Normalized Difference Vegetation Index in the Integration of Conservation Education." Indonesian Journal of Science and Technology 3, no. 1 (2018): 47. http://dx.doi.org/10.17509/ijost.v3i1.10798.
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Forest plays an important role to support a global environment. Currently, forest degradation occurs in developing countries. Therefore, the excellent strategies to against the forest degradation must be found. One of the best solutions is understanding the information of vegetation condition. Here, the objective of this paper was to apply a method as the assessment of vegetation monitoring using satellite data in the integration of conservation education forest at great forest Wan Abdul Rachman in Lampung Province, Indonesia. In this study, normalized difference vegetation index (NDVI) was used, completed with satellite data (namely MODIS). This technique helps in monitoring vegetation status. Data NDVI from MODIS satellite data showed that forest area decrease very small from 2000-2017. The data was obtained for June, July, and the end of September.
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Xu, Nianxu, Jia Tian, Qingjiu Tian, Kaijian Xu, and Shaofei Tang. "Analysis of Vegetation Red Edge with Different Illuminated/Shaded Canopy Proportions and to Construct Normalized Difference Canopy Shadow Index." Remote Sensing 11, no. 10 (2019): 1192. http://dx.doi.org/10.3390/rs11101192.
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Shadows exist universally in sunlight-source remotely sensed images, and can interfere with the spectral morphological features of green vegetations, resulting in imprecise mathematical algorithms for vegetation monitoring and physiological diagnoses; therefore, research on shadows resulting from forest canopy internal composition is very important. Red edge is an ideal indicator for green vegetation’s photosynthesis and biomass because of its strong connection with physicochemical parameters. In this study, red edge parameters (curve slope and reflectance) and the normalized difference vegetation index (NDVI) of two species of coniferous trees in Inner Mongolia, China, were studied using an unmanned aerial vehicle’s hyperspectral visible-to-near-infrared images. Positive correlations between vegetation red edge slope and reflectance with different illuminated/shaded canopy proportions were obtained, with all R2s beyond 0.850 (p < 0.01). NDVI values performed steadily under changes of canopy shadow proportions. Therefore, we devised a new vegetation index named normalized difference canopy shadow index (NDCSI) using red edge’s reflectance and the NDVI. Positive correlations (R2 = 0.886, p < 0.01) between measured brightness values and NDCSI of validation samples indicated that NDCSI could differentiate illumination/shadow circumstances of a vegetation canopy quantitatively. Combined with the bare soil index (BSI), NDCSI was applied for linear spectral mixture analysis (LSMA) using Sentinel-2 multispectral imaging. Positive correlations (R2 = 0.827, p < 0.01) between measured brightness values and fractional illuminated vegetation cover (FIVC) demonstrate the capacity of NDCSI to accurately calculate the fractional cover of illuminated/shaded vegetation, which can be utilized to calculate and extract the illuminated vegetation canopy from satellite images.
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Lattu, Arny, Habi Baturohmah, Adhitia Erfina, Sudin Saepudin, and Falentino Sembiring. "Identification of land drought potential in Blora district using NDVI and NDWI methods." BIO Web of Conferences 148 (2024): 03003. https://doi.org/10.1051/bioconf/202414803003.
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The plumbing system is an inseparable part of a multi-storey building. Drought is one of the routine disasters occurring in Indonesia. The occurrence of a prolonged disaster caused by the absence of countermeasures and good handling from the government. One of the causes of the drought is seasonal change. Blora district is one of the districts in Central Java that suffers severe drought other than other areas in Central Java which has two seasons: dry season and rainy season. The turn of the season resulted in a long drought in the Blora area. The purpose of this research is to identify the possibility of drought in Blora district by looking at several variables of drought index that is, vegetation index, wetness index and rainfall. To measure the vegetation index using NDVI (Normalized Difference Vegetation Index) method and for measure the wetness index using NDWI (Normalized Difference Water Index) method. The results of this research suggest that there is a relationship between vegetation index, wetness index and rainfall. When the value of wetness index increases then the vegetation index gets more solid. Like the data obtained in April 2016, the vegetation index in Blora District is -0.118 due to the dry season and the minimum rainfall that month.
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Hu, Jingbo, Xin Du, Qiangzi Li, et al. "Aboveground Biomass Estimation of Highland Barley in Qinghai–Tibet Plateau—Exploring the Advantages of Time Series Data and Terrain Effects." Remote Sensing 17, no. 4 (2025): 655. https://doi.org/10.3390/rs17040655.
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The timely and precise estimation of crop aboveground biomass (AGB) is crucial for evaluating crop development and forecasting yields. The objective is to examine the differences, advantages, and limitations between time series parameters and single-time-phase indicators derived from various vegetation indices in AGB estimation. Moreover, we aim to quantitatively investigate and elucidate the impact of the topographic and geographic conditions of the study region on the estimation of highland barley AGB. Results indicate that AGB simulations utilizing time series parameters from vegetation index time series (VI-TS) curves yield satisfactory results for all three VIs, with the exception of the Normalized Difference Vegetation Index (NDVI), which encounters saturation issues. The performance metrics are as follows: the Enhanced Vegetation Index (EVI) (R2 = 0.73, RMSE = 20.24 g/m2), the Soil-Adjusted Vegetation Index (SAVI) (R2 = 0.67, RMSE = 20.97 g/m2), and the Normalized Difference Mountain Vegetation Index (NDMVI) (R2 = 0.54, RMSE = 24.92 g/m2). The inclusion of our quantitative terrain factor improves the simulation accuracies of NDVI, SAVI, and NDMVI. Overall, the terrain factor has a beneficial impact on the highland barley AGB simulation outcomes. This study establishes a foundational framework for the timely and precise estimation of highland barley biomass, crucial for monitoring agricultural production in plateau mountainous regions.
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Gulácsi, András, and Ferenc Kovács. "Drought Monitoring With Spectral Indices Calculated From Modis Satellite Images In Hungary." Journal of Environmental Geography 8, no. 3-4 (2015): 11–20. http://dx.doi.org/10.1515/jengeo-2015-0008.
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Abstract In this study a new remote sensing drought index called Difference Drought Index (DDI) was introduced. DDI was calculated from the Terra satellite’s MODIS sensor surface reflectance data using visible red, near-infrared and short-wave-infrared spectral bands. To characterize the biophysical state of vegetation, vegetation and water indices were used from which drought indices can be derived. The following spectral indices were examined: Difference Vegetation Index (DVI), Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Difference Water Index (DWI), Normalized Difference Water Index (NDWI), Difference Drought Index (DDI) and Normalized Difference Drought Index (NDDI). Regression analysis with the Pálfai Drought Index (PaDi) and average annual yield of different crops has proven that the Difference Drought Index is applicable in quantifying drought intensity. However, after comparison with reference data NDWI performed better than the other indices examined in this study. It was also confirmed that the water indices are more sensitive to changes in drought conditions than the vegetation ones. In the future we are planning to monitor drought during growing season using high temporal resolution MODIS data products.
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Slamet, B., O. K. H. Syahputra, H. Kurniawan, M. Saraan, and M. M. Harahap. "Analysis of vegetation cover and built-up areas in the Percut watershed landscape, North Sumatra Province using sentinel-2 imagery." IOP Conference Series: Earth and Environmental Science 912, no. 1 (2021): 012089. http://dx.doi.org/10.1088/1755-1315/912/1/012089.
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Abstract Changes in land cover have an impact on the health condition of a watershed. This research was conducted by utilizing Sentinel-2 imagery for the recording period 2020 and 2021. Three indices were used in this study, namely, the Normalized Difference Built-up Index (NDBI), Normalized Difference Water Index (NDWI) and Normalized Difference Vegetation Index (NDVI). NDBI analysis indicates there is an increase in the built-up area of 2,092.62 hectares which means land conversion. NDWI classification shows an increase in the wetness area of 308.58 hectares, mainly occurring in the downstream part of the watershed, located to the north. There is an increase in the area of non-vegetated areas reaching 288.96 hectares in the Percut watershed based on the results of the NDVI analysis.
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Santana, Níckolas. "Fire Recurrence and Normalized Difference Vegetation Index (NDVI) Dynamics in Brazilian Savanna." Fire 2, no. 1 (2018): 1. http://dx.doi.org/10.3390/fire2010001.
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Fire is one of the main modeling agents of savanna ecosystems, affecting their distribution, physiognomy and species diversity. Changes in the natural fire regime on savannas cause disturbances in the structural characteristics of vegetation. Theses disturbances can be effectively monitored by time series of remote sensing data in different terrestrial ecosystems such as savannas. This study used trend analysis in NDVI (Normalized Difference Vegetation Index)–MODIS (Moderate Resolution Imaging Spectroradiometer) time series to evaluate the influence of different fire recurrences on vegetation phenology of the Brazilian savanna in the period from 2001 to 2016. The trend analysis indicated several factors responsible for changes in vegetation: (a) The absence of fire in savanna phytophysiognomies causes a constant increase in MODIS–NDVI, ranging from 0.001 to 0.002 per year, the moderate presence of fire in these areas does not cause significant changes, while the high recurrence results in decreases of MODIS–NDVI, ranging from −0.002 to −0.008 per year; (b) Forest areas showed a high decrease in NDVI, reaching up to −0.009 MODIS–NDVI per year, but not related to fire recurrence, indicating the high degradation of these phytophysiognomies; (c) Changes in vegetation are highly connected to the protection status of the area, such as areas of integral protection or sustainable use, and consequently their conservation status. Areas with greater vegetation conservation had more than 70% of positive changes in pixels with significant tendencies. Absence or presence of fire are the main agents of vegetation change in areas with lower anthropic influence. These results reinforce the need for a suitable fire management policy for the different types of Cerrado phytophysiognomies, in addition to highlighting the efficiency of remote sensing time series for evaluation of vegetation phenology.
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Zhao, Xuan, Jianjun Liu, and Yuankun Bu. "Quantitative Analysis of Spatial Heterogeneity and Driving Forces of the Thermal Environment in Urban Built-up Areas: A Case Study in Xi’an, China." Sustainability 13, no. 4 (2021): 1870. http://dx.doi.org/10.3390/su13041870.
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Clarifying the spatial heterogeneity of urban heat island (UHI) effect is of great significance for promoting sustainable urban development. A GeoDetector was used to detect the influential natural and society factors. Natural factors (normalized difference vegetation index (NDVI), soil-regulating vegetation index (SAVI), normalized building index (NDBI), and modified normalized difference water index (MNDWI)) as well as society factors (road density (RDD), and population density (POPD)) were selected as driving factors to be tested for their explanatory power for land surface temperature (LST). Results indicated that the Moran’s I index value for the LST of the built-up area is 0.778. The top three factors influencing the LST were NDBI, NDVI, and SAVI, the explanatory power of which was 0.7593, 0.6356, and 0.6356, respectively. The interactive explanatory power for NDBI and MNDWI was 0.8108 and for NDBI and RDD was 0.8002, these two interactions are double enhanced interaction relationships. The results of this study play a guiding role in the development of urban thermal environment regulation schemes and ecological environment planning.
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Olotu, Y., D.A. Okudugha, N.L. Izah, O. Ikhide, and C.I. Ayilaran. "Estimation of Groundnut Water Requirements using Arc-GIS Normalized Difference Vegetation Index." Journal of Scientific and Engineering Research 9, no. 4 (2022): 142–49. https://doi.org/10.5281/zenodo.10519346.
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<strong>Abstract</strong> The 83-day experimentation which covered four months (August-Aug., September-Sept., October-Oct., and November-Nov.) was conducted beside the mini-hydrological station at campus-one, Auchi Polytechnic to estimate the crop water requirements (CWR) of groundnut (<em>Arachis hypogaea </em>L.) using Normalized Difference Index (NDVI) on sandy loamy soil at Auchi, Edo State. The results showed that Normalized Difference Vegetation Index (NDVI) produced weak over a groundnut field of 10 m * 10 m. The result showed NDVI-CWR estimated values of 2.03 mm, 9.6 mm, 10.9 mm, and 6.5 mm for the months of Aug., Sept., Oct., and November. The poor performance of the NDVI technique could be attributed to the smaller size of the groundnut field, spatial resolution, and field coordinates. Therefore, green-seeker sensor is preferable to NDVI over a smaller field, while NDVI could be very effective on the large field.
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& Al-Jibouri, Khalaf. "DETECTION LAND COVER CHANGES OF THE BAQUBA CITY FOR THE PERIOD 2014-2019 USING SPECTRAL INDICES." IRAQI JOURNAL OF AGRICULTURAL SCIENCES 51, no. 3 (2020): 805–15. http://dx.doi.org/10.36103/ijas.v51i3.1036.
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This study was conducted on the Land coverings of the city of Baquba and its outskirts in Diyala province, central Iraq, between latitudes 44º 42ʹ 31.78ʺ ــ 44º33ʹ 14.99ʺ and 33º41ʹ 46.66ʺ ــ 33º 48ʹ 23.18ʺ an area of 180,835 km2. In order to classify the earth covers, it was relied on the field survey to determine the grounding points. Also used two satellite data from Landsat 8, the first one on 23/3/2014, the second on 21/3/2019, and the production of Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI) and Normalized Differences Built- up the Index (NDBI) maps. The results of the survey was showed five varieties are vegetation cover, agricultural land, water, buildings and barren land. They were identified and compared with the 75 land control points, The accuracy of the classification was calculated using Kappa It was 89% , and purely concluded that the use of manuals NDVI, NDWI and NDBI was useful for classifying Land coverings and detecting changes as they are considered an easy and fast method.
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Memon, Muhammad Sohail, Zhou Jun, Chuanliang Sun, et al. "Assessment of Wheat Straw Cover and Yield Performance in a Rice-Wheat Cropping System by Using Landsat Satellite Data." Sustainability 11, no. 19 (2019): 5369. http://dx.doi.org/10.3390/su11195369.
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Proper straw cover information is one of the most important inputs for agroecosystem and environmental modeling, but the availability of accurate information remains limited. However, several remote-sensing (RS)-based studies have provided a residue cover estimation and provided spatial distribution mapping of paddy rice areas in a constant field condition. Despite this, the performance of rice crops with straw applications has received little attention. Furthermore, there are no methods currently available to quantify the wheat straw cover (WSC) percentage and its effect on rice crops in the rice-wheat cropping region on a large scale and a continuous basis. The novel approach proposed in this study demonstrates that the Landsat satellite data and seven RS-based indices, e.g., (i) normalized difference vegetation index (NDVI), (ii) Normalized difference senescent vegetation index (NDSVI), (iii) Normalized difference index 5 (NDI5), (iv) Normalized difference index 7 (NDI7), (v) Simple tillage index (STI), (vi) Normalized difference tillage index (NDTI), and (vii) Shortwave red normalized difference index (SRNDI), can be used to estimate the WSC percentage and determine the performance of rice crops over the study area in Changshu county, China. The regression model shows that the NDTI index performed better in differentiating the WSC at sampling points with a coefficient of determination (R2 = 0.80) and root mean squared difference (RMSD = 8.46%) compared to that of other indices, whereas the overall accuracy for mapping WSC was observed to be 84.61% and the kappa coefficient was κ = 0.76. Moreover, the rice yield model was established by correlating between the peak NDVI values and rice grain yield collected from ground census data, with R2 = 0.85. The finding also revealed that the highest estimated yield (8439.67 kg/ha) was recorded with 68% WCS in the study region. This study confirmed that the NDVI and NDTI algorithms are very effective and robust indicators. Also, it can be strongly concluded that multispectral Landsat satellite imagery is capable of measuring the WSC percentage and successively determines the impact of different WSC percentages on rice crop yield within fields or across large regions through remote sensing (RS) and geographical information system (GIS) techniques for the long-term planning of agriculture sustainability in rice-wheat cropping systems.
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Bevz, Svetlana Ya, Anna M. Kozina, and Tatyana V. Voblikova. "Normalized Difference Vegetation Index (NDVI) in Assessment of Grain Crop State." BIO Web of Conferences 108 (2024): 09003. http://dx.doi.org/10.1051/bioconf/202410809003.
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The results of studying the dynamics of the NDVI of winter triticale crops in the conditions of the Northwest of Russia are presented. The NDVI was determined based on remote sensing data obtained by two methods including satellite imagery and aerial photography from an unmanned aerial vehicle (UAV). The analysis of the dynamics of the NDVI of the surveyed area for the period from May 15 to August 8, obtained from the Sentinel-2 L2A satellite, showed the dependence of the index on the increase in biomass of winter triticale by the periods of its growth and development, as well as on the meteorological conditions of the growing seasons. The sharpest increase in the vegetation index was noted in the period from the beginning of June to the beginning of July, which corresponds to the phase of entering the tube — filling of winter triticale grain. In the most favorable weather conditions, the vegetation index reached 1.0, which indicates the formation of dense vegetation by triticale crops. It is confirmed that the strong dependence of the average NDVI on yield occurs against the background of a decrease in the value of the vegetation index. The conducted aerial photography showed the presence of spatial heterogeneity in the studied fields, which caused uneven growth and development of winter triticale plants within the boundaries of one field. The shortage of grain from each hectare will be about 200 kg. In this regard, it is recommended to apply precision farming using NDVI mapping schemes with reference to point-based field surveys.
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Sathyaseelan, M., S. K. Ghosh, and C. S. P. Ojha. "ENVIRONMENTAL SUSTAINABILITY ASSESSMENT OF A HIMALAYAN CATCHMENT WITH LAND COVER INDICES AND LST RELATIONSHIP USING PRINCIPAL COMPONENT ANALYSIS – A GEOSPATIAL APPROACH." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-M-1-2023 (April 21, 2023): 285–92. http://dx.doi.org/10.5194/isprs-archives-xlviii-m-1-2023-285-2023.
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Abstract. Environmental sustainability assessment is a crucial part of the management of natural resources. Remote Sensing based environmental land cover indices such as Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Built-up Index (NDBI), Normalized Difference Moisture Index (NDMI), and its associated Land Surface Temperature (LST) are the major governing factors for the environmental processes that happen on the surface of the earth . These NDVI, NDWI, NDBI, NDMI, and LST are generated for 2020 using the Landsat satellite datasets. The process-based relationship among them is complex and involves various parameters but may be easily represented by multiple linear regression models. Principal Component Analysis (PCA) is one such type that efficiently handles and evaluates the contribution of each of these factors to each other based on the sampling units. The study area is the upper Ramganga catchment in the Indian Himalayas, consisting of 117 sub-catchments. These catchment units (samples) are entangled with these environmental factors. The results of the PCA reveal the relationship between each of the environmental factors and their priority. Based on the uncorrelated factors priority suggestion from the PCA, catchment units were classified as high, moderate, or low categories based on their dominance in the relationship among the factors. These spatial variations in the environmental factors can help to assess the sustainability of resources in the Himalayan catchment.
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Peña-Molina, Esther, Daniel Moya, Eva Marino, et al. "Fire Vulnerability, Resilience, and Recovery Rates of Mediterranean Pine Forests Using a 33-Year Time Series of Satellite Imagery." Remote Sensing 16, no. 10 (2024): 1718. http://dx.doi.org/10.3390/rs16101718.
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The modification of fire regimes and their impact on vegetation recovery, soil properties, and fuel structure are current key research areas that attempt to identify the thresholds of vegetation’s susceptibility to wildfires. This study aimed to evaluate the vulnerability of Mediterranean pine forests (Pinus halepensis Mill. and Pinus pinaster Aiton) to wildfires, analyzing two major forest fires that occurred in Yeste (Spain) in 1994 and 2017, affecting over 14,000 and 3200 hectares, respectively. Four recovery regions were identified based on fire severity—calculated using the delta Normalized Burn Ratio (dNBR) index—and recurrence: areas with high severity in 2017 but not in 1994 (UB94-HS17), areas with high severity in 1994 but not in 2017 (HS94-UB17), areas with high severity in both fires (HS94-HS17), and areas unaffected by either fire (UB94-UB17). The analysis focused on examining the recovery patterns of three spectral indices—the Normalized Difference Vegetation Index (NDVI), Normalized Moisture Index (NDMI), and Normalized Burn Ratio (NBR)—using the Google Earth Engine platform from 1990 to 2023. Additionally, the Relative Recovery Indicator (RRI), the Ratio of Eighty Percent (R80P), and the Year-on-Year average (YrYr) metrics were computed to assess the spectral recovery rates by region. These three spectral indices showed similar dynamic responses to fire. However, the Mann–Kendall and unit root statistical tests revealed that the NDVI and NDMI exhibited distinct trends, particularly in areas with recurrence (HS94-HS17). The NDVI outperformed the NBR and NDMI in distinguishing variations among regions. These results suggest accelerated vegetation spectral regrowth in the short term. The Vegetation Recovery Capacity After Fire (VRAF) index showed values from low to moderate, while the Vulnerability to Fire (V2FIRE) index exhibited values from medium to high across all recovery regions. These findings enhance our understanding of how vegetation recovers from fire and how vulnerable it is to fire.
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Luo, Jianxing. "Study on the Impact of MODIS-derived NDVI and NDBI on Land Surface Temperature." Highlights in Science, Engineering and Technology 69 (November 6, 2023): 249–58. http://dx.doi.org/10.54097/hset.v69i.11911.
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With the development and expansion of cities, the increase in built-up area and the drop in vegetation coverage have significant influences on the climatic environment of urban areas. This study processed Moderate Resolution Imaging Spectroradiometer (MODIS) data from four months in 2001 to investigate the Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-Up Index (NDBI), and Land Surface Temperature (LST) in the Chengdu region. This paper explored the correlation between NDVI and LST, as well as the correlation between NDBI and LST. The findings revealed that areas with higher NDBI, indicating a greater extent of urban built-up areas, exhibited higher daytime and nighttime LST, indicating a more pronounced urban heat island effect. Conversely, an increase in vegetation cover was found to lead to a decrease in surface temperature and a certain degree of mitigation of the urban heat island effect. Furthermore, a good linear relationship was observed between NDBI and LST, with a stronger correlation during the daytime compared to the nighttime.
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Macarof, Paul, and Florian Statescu. "Comparasion of NDBI and NDVI as Indicators of Surface Urban Heat Island Effect in Landsat 8 Imagery: A Case Study of Iasi." Present Environment and Sustainable Development 11, no. 2 (2017): 141–50. http://dx.doi.org/10.1515/pesd-2017-0032.
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Abstract This study compares the normalized difference built-up index (NDBI) and normalized difference vegetation index (NDVI) as indicators of surface urban heat island effects in Landsat-8 OLI imagery by investigating the relationships between the land surface temperature (LST), NDBI and NDVI. The urban heat island (UHI) represents the phenomenon of higher atmospheric and surface temperatures occurring in urban area or metropolitan area than in the surrounding rural areas due to urbanization. With the development of remote sensing technology, it has become an important approach to urban heat island research. Landsat data were used to estimate the LST, NDBI and NDVI from four seasons for Iasi municipality area. This paper indicates than there is a strong linear relationship between LST and NDBI, whereas the relationship between LST and NDVI varies by season. This paper suggests, NDBI is an accurate indicator of surface UHI effects and can be used as a complementary metric to the traditionally applied NDVI.
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Idrisa, Hamimu, S. A. Kadam, and S. D. Gorantiwar. "Estimation of Crop Coefficients Based on Normalize Difference Vegetation Index." Journal of Agriculture Research and Technology 47, no. 03 (2022): 381–88. http://dx.doi.org/10.56228/jart.2022.47320.
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Crop coefficient is one of the most important parameters used for the estimation of crop evapotranspiration (ETc). Crop coefficient (Kc)-based estimation of crop evapotranspiration is most commonly used methods for irrigation water management. However, crop coefficient approach used for estimation ETc using the generalized crop coefficients mentioned in Irrigation and Drainage Paper No. 56 of the Food and Agricultural Organization of the United Nations can contribute to crop evapotranspiration estimates that are substantially different from actual crop evapotranspiration. The colinear relationship between the crop coefficient curve and a satellitederived Normalized Difference Vegetation Index (NDVI) showed potential for modeling a crop coefficient as a function of the NDVI, which is also one among the methods used for estimation of ETc in irrigation water management. The present study was conducted with objectives to present the techniques and procedures to develop and estimates Kc based on vegetation index (NDVI) extracted from satellite data. The relationships between and NDVI and crop coefficients (Kc) of wheat and chickpea for corresponding months were developed. The regression models developed are: (Kc) NDVI = 6.3268*NDVI-1.4207 for wheat and (Kc) NDVI = 5.7866 * NDVI-1.6699 for chickpea. The models showed strong relationships with R2= 0.86 and R2=0.84 for wheat and chickpea, respectively. The model and techniques to develop and estimate crop coefficients can be used in other regions in the global, and hence estimate crop evapotranspiration. The crop coefficients (Kc) estimated based on NDVI are useful for irrigation scheduling, evaluating irrigation performance, irrigation water management, and estimation of water use efficiency.
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Dhaloiya, Arvind, Darshana Duhan, DM Denis, Dharmendra Singh, Mukesh Kumar, and Manender Singh. "Modeling medium resolution evapotranspiration using downscaling techniques in north-western part of India." MAUSAM 74, no. 3 (2023): 561–78. http://dx.doi.org/10.54302/mausam.v74i3.5112.
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The present investigation provides a modeling solution to downscale MODIS-based evapotranspiration (ET) at a 30 m spatial resolution from its original 500 m spatial resolution using meteorological and Landsat 8 (Operational Land Imager, OLI) data by employing downscaling models. The nine indices namely Surface Albedo, Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), Modified Soil-Adjusted Vegetation Index (MSAVI), Normalized Difference Built-up Index (NDBI), Normalized Difference Water Index (NDWI), Normalized Difference Moisture Index (NDMI), and Normalized Difference Infrared Index for Band 7 (NDIIB7) were calculated from Lansat 8 data at 30 m spatial resolution. The multiple linear regression (MLR) and Least Square Support Vector Machine (LS-SVM) models were developed to generate the relationship between MODIS 500 m ET and Landsat indices at 500 m scale. Further, these develop models were used to estimate 30 m ET based on 30 m Landsat 8 indices. The performance of developed models (MLR and LS-SVM) was carried out using correlation coefficient (CC), Nash-Sutcliffe coefficient (NASH) efficiency, Root Mean Square Error (RMSE) and Normalised Mean Square Error (NMSE). Penman–Monteith (PM) method was used to estimate the ET using observed station data. The results show that lowest ETO was observed in the month of December while it was maximum in the month of May. Using the performances indices, it was found that LS-SVM model slightly outperformed than MLR model. However, the downscaled model overestimates ET in comparison to the Penman-Monteith method. Further, the significant correlation was found between MODIS ET and LS-SVM ET at all the stations.