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Journal articles on the topic 'Vegetation series'

Author: Grafiati

Published: 6 July 2024

Last updated: 31 July 2025

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1

Simoniello, T., M. Lanfredi, M. Liberti, R. Coppola, and M. Macchiato. "Estimation of vegetation cover resilience from satellite time series." Hydrology and Earth System Sciences Discussions 5, no. 1 (2008): 511–46. http://dx.doi.org/10.5194/hessd-5-511-2008.

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Abstract. Resilience is a fundamental concept for understanding vegetation as a dynamic component of the climate system. It expresses the ability of ecosystems to tolerate disturbances and to recover their initial state. Recovery times are basic parameters of the vegetation's response to forcing and, therefore, are essential for describing realistic vegetation within dynamical models. Healthy vegetation tends to rapidly recover from shock and to persist in growth and expansion. On the contrary, climatic and anthropic stress can reduce resilience thus favouring persistent decrease in vegetation

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Simoniello, T., M. Lanfredi, M. Liberti, R. Coppola, and M. Macchiato. "Estimation of vegetation cover resilience from satellite time series." Hydrology and Earth System Sciences 12, no. 4 (2008): 1053–64. http://dx.doi.org/10.5194/hess-12-1053-2008.

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Abstract. Resilience is a fundamental concept for understanding vegetation as a dynamic component of the climate system. It expresses the ability of ecosystems to tolerate disturbances and to recover their initial state. Recovery times are basic parameters of the vegetation's response to forcing and, therefore, are essential for describing realistic vegetation within dynamical models. Healthy vegetation tends to rapidly recover from shock and to persist in growth and expansion. On the contrary, climatic and anthropic stress can reduce resilience thus favouring persistent decrease in vegetation

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Khan, Asim, Warda Asim, Anwaar Ulhaq, and Randall W. Robinson. "A deep semantic vegetation health monitoring platform for citizen science imaging data." PLOS ONE 17, no. 7 (2022): e0270625. http://dx.doi.org/10.1371/journal.pone.0270625.

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Automated monitoring of vegetation health in a landscape is often attributed to calculating values of various vegetation indexes over a period of time. However, such approaches suffer from an inaccurate estimation of vegetational change due to the over-reliance of index values on vegetation’s colour attributes and the availability of multi-spectral bands. One common observation is the sensitivity of colour attributes to seasonal variations and imaging devices, thus leading to false and inaccurate change detection and monitoring. In addition, these are very strong assumptions in a citizen scien

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Dobremez, J. F. "Vegetation classification and vegetation mapping in the Himalayas." Geobotanical mapping, no. 1994-1995 (1996): 45–50. http://dx.doi.org/10.31111/geobotmap/1994-1995.45.

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In the introduction the history of botanical exploration of the Himalayas is considered starting from the late XVIIIth century up to present time. The next part of the article is devoted to the history of vegetation mapping proper. Vegetation maps relating to the Himalayas as a whole and to its different parts are enumerated including the vegetation map of Nepal in 8 sheets at scale 1 : 2 500 000 by the author (1971 to 1985) and his two large-scale maps (1 : 50 000) covering small areas in Eastern and Central Nepal (1974 and 1977). The above vegetation maps have been constructed using the basi

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Patel, J. H., and M. P. Oza. "Deriving crop calendar using NDVI time-series." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-8 (November 28, 2014): 869–73. http://dx.doi.org/10.5194/isprsarchives-xl-8-869-2014.

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Agricultural intensification is defined in terms as cropping intensity, which is the numbers of crops (single, double and triple) per year in a unit cropland area. Information about crop calendar (i.e. number of crops in a parcel of land and their planting & harvesting dates and date of peak vegetative stage) is essential for proper management of agriculture. Remote sensing sensors provide a regular, consistent and reliable measurement of vegetation response at various growth stages of crop. Therefore it is ideally suited for monitoring purpose. The spectral response of vegetation, as meas

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Rivas-Martínez, S., and D. Sánchez-Mata. "Boreal vegetation series of North America." Plant Biosystems - An International Journal Dealing with all Aspects of Plant Biology 145, sup1 (2011): 208–19. http://dx.doi.org/10.1080/11263504.2011.602742.

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Liu, Gui Xiang, Zhuo Yi, Feng Ming Yu, and Chun Long Jiang. "Study on Effect of Drought Based on Time Series on Grassland Vegetation in Eastern Inner Mongolia." Advanced Materials Research 518-523 (May 2012): 5306–15. http://dx.doi.org/10.4028/www.scientific.net/amr.518-523.5306.

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This paper, based on the long sequence meteorological data and the MODIS remote sensing data, calculates the every-ten-day NDVI index and SPI index of the grassland vegetation in the Eastern Inner Mongolia between 2006 and 2010. It applies the SPI index to indicate the degree of drought and the NDVI index to represent the growth status of the grassland vegetation. This paper analyzes the relationship between the NDVI index and the SPI index by the Time Series Spectrum Analysis Method, leading to the conclusion that the vegetations are sensitive to the drought in the green-turning and yellowing

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Sun, Chao, Jialin Li, Luodan Cao, Yongchao Liu, Song Jin, and Bingxue Zhao. "Evaluation of Vegetation Index-Based Curve Fitting Models for Accurate Classification of Salt Marsh Vegetation Using Sentinel-2 Time-Series." Sensors 20, no. 19 (2020): 5551. http://dx.doi.org/10.3390/s20195551.

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The successful launch of the Sentinel-2 constellation satellite, along with advanced cloud detection algorithms, has enabled the generation of continuous time series at high spatial and temporal resolutions, which is in turn expected to enable the classification of salt marsh vegetation over larger spatiotemporal scales. This study presents a critical comparison of vegetation index (VI) and curve fitting methods—two key factors for time series construction that potentially influence vegetation classification performance. To accomplish this objective, the stability of five different VI time ser

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Osipov, S. V., and V. P. Verkholat. "The large-scale vegetation maps of the western coast of Peter the Great Bay (Far East, the Sea of Japan)." Geobotanical mapping, no. 1998-2000 (2000): 50–61. http://dx.doi.org/10.31111/geobotmap/1998-2000.50.

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Two territories on the western coast of Peter the Great Bay were mapped in the large scale. The geobotanical mapping means revealing and displaying the essential regularities of vegetation cover. Both the spatial and temporal regularities of vegetation under natural and anthropogenic influences are well pronounced in the territory under consideration. The concept of the vegetation spatial unit (vegetation complexes) was applied as a basis for mapping. The maps and their legend were worked out as a system of vegetation combination types (vegetation combination is a spatial unit of the supracoen

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Li, Chong, Qianzuo Zhao, Junyuan Fei, Lei Cui, Xiu Zhang, and Guodong Yin. "Prediction of Vegetation Indices Series Based on SWAT-ML: A Case Study in the Jinsha River Basin." Remote Sensing 17, no. 6 (2025): 958. https://doi.org/10.3390/rs17060958.

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Vegetation dynamics significantly influence watershed ecohydrological processes. Physically based hydrological models often have general plant development descriptions but lack vegetation dynamics data for ecohydrological simulations. Solar-induced chlorophyll fluorescence (SIF) and the Normalized Difference Vegetation Index (NDVI) are widely used in monitoring vegetation dynamics and ecohydrological research. Accurately predicting long-term SIF and NDVI dynamics can support the monitoring of vegetation anomalies and trends. This study proposed a SWAT-ML framework, combining the Soil and Water

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Yu, Rong, Bo Feng Cai, Xiang Qin Su, Ya Zi He, and Jing Yang. "Modeling Research on 1982-2000 NDVI Time Series Data of Chinese Different Vegetation Types Based on Autoregressive Moving Average Model." Advanced Materials Research 955-959 (June 2014): 863–68. http://dx.doi.org/10.4028/www.scientific.net/amr.955-959.863.

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Vegetation index time series data modeling is widely used in many research areas, such as analysis of environmental change, estimation of crop yield, and the precision of the traditional vegetation index time series data fitting model is lower. This paper conducts the modeling with introducing the autoregressive moving average time series model, and using NOAA/AVHRR normalized differential vegetation index time series data, to estimate the errors of original data which are between under the situation that the parameters to be estimated are lesser, and on the basis gives the fitted equation to

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Halip, Rowena Mat, Nik Norasma Che’Ya, Rhushalshafira Rosle, Mohd Razi Ismail, Zulkarami Berahim, and Mohamad Husni Omar. "Enhancing Rice Crop Monitoring Through UAV Imagery And GIS Analysis." IOP Conference Series: Earth and Environmental Science 1412, no. 1 (2024): 012014. https://doi.org/10.1088/1755-1315/1412/1/012014.

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Abstract Unmanned aerial vehicles (UAVs) are becoming increasingly important in many industries, and agriculture is no exception. This study aimed to monitor rice fields by using UAV-based imagery to map rice fields thoroughly and using on-the-ground Soil Plant Analysis Development (SPAD) data to evaluate crop health. A series of UAV-captured photos were analyzed as part of the inquiry, and the results included the creation of reclassification maps and Normalized Difference Vegetation Index (NDVI), which were reinforced by GIS analysis. The results show that during the 11-day post-sowing phase

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TELESCA, LUCIANO, ROSA LASAPONARA, and ANTONIO LANORTE. "DISCRIMINATING FLUCTUATION DYNAMICS IN BURNED AND UNBURNED VEGETATIONAL COVERS." Fluctuation and Noise Letters 05, no. 04 (2005): L479—L487. http://dx.doi.org/10.1142/s0219477505002914.

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Fluctuation dynamics of time series of satellite SPOT-VEGETATION Normalized Difference of Vegetation Index (NDVI) data from 1998 to 2003 were analyzed to discriminate fire-induced variability in the vegetational dynamics of shrub-land in southern Italy. We used detrended fluctuation analysis (DFA), which permits the detection of persistent properties in nonstationary signal fluctuations. We analyzed two shrub-land covers, one in "healthy conditions" (fire-unaffected) and the other in "ill conditions" (fire-affected). Our findings suggest that fires play an important role in the temporal evolut

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Najafi, Z., P. Fatehi, and A. A. Darvishsefat. "VEGETATION DYNAMICS TREND USING SATELLITE TIME SERIES IMAGERY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W18 (October 18, 2019): 783–88. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w18-783-2019.

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Abstract. In this study, the trend of vegetation dynamics in Kermanshah city assessed using NDVI MOD13Q1 product over the time period of 2000–2017. Based on time series imagery the pick of vegetation phenology stage (maximum NDVI) identified, then the trend of vegetation dynamic was investigated using the Ordinary Least Square regression and the Theil-Sen approaches. To generate a pixel-wise trend map, a pixel-based vegetation dynamics was also implemented. A non-parametric Mann-Kendall statistics approach was used to examine a statistically significant trend analysis. The mean maximum NDVI ob

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Yue, Zichen, Shaobo Zhong, Wenhui Wang, Xin Mei, and Yunxin Huang. "Phenology-Optimized Drought Index Reveals the Spatio-Temporal Patterns of Vegetation Health and Its Attribution on the Loess Plateau." Remote Sensing 17, no. 5 (2025): 891. https://doi.org/10.3390/rs17050891.

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Frequent droughts pose a severe threat to the ecological health and sustainable development of the Loess Plateau (LP). The accurate assessment of the impact of drought on vegetation is crucial for diagnosing ecological health. Traditional drought assessment methods often rely on coarse estimations based on averages of vegetation drought indices, overlooking the spatial differentiation of complex vegetation phenology. This study proposes a vegetative drought assessment method that considers vegetation phenological characteristics using MODIS EVI and LST data products. First, the start and end o

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Lan, Shengxin, and Zuoji Dong. "Incorporating Vegetation Type Transformation with NDVI Time-Series to Study the Vegetation Dynamics in Xinjiang." Sustainability 14, no. 1 (2022): 582. http://dx.doi.org/10.3390/su14010582.

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Time-series normalized difference vegetation index (NDVI) is commonly used to conduct vegetation dynamics, which is an important research topic. However, few studies have focused on the relationship between vegetation type and NDVI changes. We investigated changes in vegetation in Xinjiang using linear regression of time-series MOD13Q1 NDVI data from 2001 to 2020. MCD12Q1 vegetation type data from 2001 to 2019 were used to analyze transformations among different vegetation types, and the relationship between the transformation of vegetation type and NDVI was analyzed. Approximately 63.29% of t

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S, Arun. "Principal Component Analysis for Evaluating the Seasonal Vegetation Anomalies from MODIS NDVI Time-series Datasets." International Journal for Research in Applied Science and Engineering Technology 11, no. 3 (2023): 1453–60. http://dx.doi.org/10.22214/ijraset.2023.49693.

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Abstract: In this paper, the author presents a method for analyzing vegetative seasonal (SW) anomalies using principal component analysis (PCA). The analysis was done on the Seasonal Maximum Value Composite of MODIS/VEGETATION NDVI obtained for the Northern Karnataka region over a spatiotemporal period (2000-2019). With multi-temporal data sets, the PCA was applied as a data transform to highlight areas of localized change. The objective is to monitor vegetation and evaluate land degradation in the Northern Karnataka Region. Although they offer broad spatial coverage and internal consistency o

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Liu, Yu, Wenqing Li, Li Li, and Naiqun Zhang. "Extraction of Long Time-Series Vegetation Indices from Combined Multisource Satellite Imagery." Computational Intelligence and Neuroscience 2022 (May 30, 2022): 1–8. http://dx.doi.org/10.1155/2022/3901372.

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Extracting vegetation cover information by combining multisource satellite images can improve the time scale of vegetation cover monitoring, realize encrypted observation in short period, and shorten the regional vegetation remote sensing monitoring cycle. The NDVI and RVI datasets from 2007–2019 were extracted using 9 phases of multisource satellite images (Landsat TM/OLI, Sentinel-2 MSI, and GF-1 PMS) covering Xiaxi, Sichuan. Three typical validation sites representing higher vegetation cover in mountains and no vegetation cover in water bodies in the region, respectively, were selected to e

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McGeehan, Steven L. "Impact of Waste Materials and Organic Amendments on Soil Properties and Vegetative Performance." Applied and Environmental Soil Science 2012 (2012): 1–11. http://dx.doi.org/10.1155/2012/907831.

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Waste materials, and materials derived from wastes, possess many characteristics that can improve soil fertility and enhance crop performance. These materials can be particularly useful as amendments to severely degraded soils associated with mining activities. This study evaluated biosolids, composts, log yard wastes, and two organic soil treatments for improved soil fertility and vegetative performance using side-by-side comparisons. Each plot was seeded with a standardized seed mix and evaluated for a series of soil chemical and physical parameters, total vegetation response, species divers

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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. T

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Zibzeev, E. G. "High-mountain vegetation of the southeastern part of Tigiretsk Ridge (West Altai)." Vegetation of Russia, no. 6 (2004): 23–34. http://dx.doi.org/10.31111/vegrus/2004.06.23.

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A high-mountain vegetation research has been carried out in the southeastern part of the Tigiretsk Ridge (the Altai Mts.). The observed communities totally refer to 14 formations, 10 floristic-coenotic types, and 4 ecological-historical series. Communities of the subalpine vegetation belt and the alpine meadows form the cryo-mesophilous and the cryo-hemihygrophilous ecological-historical series, respectively. Vegetation of mountain tundras is represented by the cryo-hemixerophilous and the cryo-hygrophilous series.

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Maignan, F., F. M. Bréon, F. Chevallier, et al. "Evaluation of a Global Vegetation Model using time series of satellite vegetation indices." Geoscientific Model Development 4, no. 4 (2011): 1103–14. http://dx.doi.org/10.5194/gmd-4-1103-2011.

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Abstract. Atmospheric CO2 drives most of the greenhouse effect increase. One major uncertainty on the future rate of increase of CO2 in the atmosphere is the impact of the anticipated climate change on the vegetation. Dynamic Global Vegetation Models (DGVM) are used to address this question. ORCHIDEE is such a DGVM that has proven useful for climate change studies. However, there is no objective and methodological way to accurately assess each new available version on the global scale. In this paper, we submit a methodological evaluation of ORCHIDEE by correlating satellite-derived Vegetation

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Bellone, Tamara, Piero Boccardo, and Francesca Perez. "Investigation of Vegetation Dynamics using Long-Term Normalized Difference Vegetation Index Time-Series." American Journal of Environmental Sciences 5, no. 4 (2009): 460–66. http://dx.doi.org/10.3844/ajessp.2009.460.466.

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Bellone. "Investigation of Vegetation Dynamics using Long-Term Normalized Difference Vegetation Index Time-Series." American Journal of Environmental Sciences 5, no. 4 (2009): 461–67. http://dx.doi.org/10.3844/ajessp.2009.461.467.

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LI, Yunqing, Kazuhiko OHNUMA, and Yoshizumi YASUDA. "Analysis of Chinese vegetation properties by time series changes of global vegetation index." Journal of the Japan society of photogrammetry and remote sensing 29, no. 1 (1990): 4–12. http://dx.doi.org/10.4287/jsprs.29.4.

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Liu, Hualiang, Feizhou Zhang, Lifu Zhang, Yukun Lin, Siheng Wang, and Yefeng Xie. "UNVI-Based Time Series for Vegetation Discrimination Using Separability Analysis and Random Forest Classification." Remote Sensing 12, no. 3 (2020): 529. http://dx.doi.org/10.3390/rs12030529.

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Land cover data is crucial for earth system modelling, natural resources management, and conservation planning. Remotely sensed time-series data capture dynamic behavior of vegetation, and have been widely used for land cover mapping. Temporal profiles of vegetation index (VI), especially normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI), are the most used features derived from time-series spectral data. Whether NDVI or EVI is optimal to generate temporal profiles has not been evaluated. The universal normalized vegetation index (UNVI), a relatively new index wi

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Kalibernova, N. M. "Showing the vegetation cover of flood-plains and river valleys on the Vegetation Map of Kazakhstan and middle Asia." Geobotanical mapping, no. 1993 (1995): 58–66. http://dx.doi.org/10.31111/geobotmap/1993.58.

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The fragment of the legend of the map concerning the vegetation of flood- plains and river-valleys in the subzones of northern and southern deserts is presented in the article. The map is compiled in Department of Vegetspon Geography and Cartography of Komarov Botanical Institute by a large team of botanists-geographers of the former USSR. The nature environments determining the development of vegetation of river-valleys in arid climate are conditioned by the intrazonal factors (alluvial and flood processes) at the background of natural zonal factors. Contrasts of environments and correspondin

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Li, Nan, Todd H. Skaggs, and Elia Scudiero. "In-Season Estimation of Japanese Squash Using High-Spatial-Resolution Time-Series Satellite Imagery." Sensors 25, no. 7 (2025): 1999. https://doi.org/10.3390/s25071999.

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Yield maps and in-season forecasts help optimize agricultural practices. The traditional approaches to predicting yield during the growing season often rely on ground-based observations, which are time-consuming and labor-intensive. Remote sensing offers a promising alternative by providing frequent and spatially extensive information on crop development. In this study, we evaluated the feasibility of high-resolution satellite imagery for the early yield prediction of an under-investigated crop, Japanese squash (Cucurbita maxima), in a small farm in Hollister, California, over the growing seas

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Li, Huayu, Jianhua Wan, Shanwei Liu, Hui Sheng, and Mingming Xu. "Wetland Vegetation Classification through Multi-Dimensional Feature Time Series Remote Sensing Images Using Mahalanobis Distance-Based Dynamic Time Warping." Remote Sensing 14, no. 3 (2022): 501. http://dx.doi.org/10.3390/rs14030501.

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Efficient methodologies for vegetation-type mapping are significant for wetland’s management practices and monitoring. Nowadays, dynamic time warping (DTW) based on remote sensing time series has been successfully applied to vegetation classification. However, most of the previous related studies only focused on Normalized Difference Vegetation Index (NDVI) time series while ignoring multiple features in each period image. In order to further improve the accuracy of wetland vegetation classification, Mahalanobis Distance-based Dynamic Time Warping (MDDTW) using multi-dimensional feature time s

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Khosravirad, M., M. Omid, F. Sarmadian, and S. Hosseinpour. "PREDICTING SUGARCANE YIELDS IN KHUZESTAN USING A LARGE TIME-SERIES OF REMOTE SENSING IMAGERY REGION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W18 (October 18, 2019): 645–48. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w18-645-2019.

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Abstract. This study aimed to evaluate the power of various vegetation indices for sugarcane yield modelling in Shoeibeyeh area in Khuzestan province of Iran. Seven indices were extracted from satellite images and were then converted to seven days' time-series via interpolation. To eliminate noise from the time-series data, all of them were reconstructed using the Savitzky-Golay algorithm. Thus seven different time-series of vegetation indices were obtained. The growth profile was drawn via averaging of NDVI time-series data and was divided into three growth intervals. Then the accumulative va

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León-Tavares, Jonathan, Jean-Louis Roujean, Bruno Smets, Erwin Wolters, Carolien Toté, and Else Swinnen. "Correction of Directional Effects in VEGETATION NDVI Time-Series." Remote Sensing 13, no. 6 (2021): 1130. http://dx.doi.org/10.3390/rs13061130.

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Land surface reflectance measurements from the VEGETATION program (SPOT-4, SPOT-5 and PROBA-V satellites) have led to the acquisition of consistent time-series of Normalized Difference Vegetation Index (NDVI) at a global scale. The wide imaging swath (>2000 km) of the family of VEGETATION space-borne sensors ensures a daily coverage of the Earth at the expense of a varying observation and illumination geometries between successive orbit overpasses for a given target located on the ground. Such angular variations infer saw-like patterns on time-series of reflectance and NDVI. The presence of

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Dai, Xue, Guishan Yang, Desheng Liu, and Rongrong Wan. "Vegetation Carbon Sequestration Mapping in Herbaceous Wetlands by Using a MODIS EVI Time-Series Data Set: A Case in Poyang Lake Wetland, China." Remote Sensing 12, no. 18 (2020): 3000. http://dx.doi.org/10.3390/rs12183000.

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The carbon sequestration capacity of wetland vegetation determines carbon stocks and changes in wetlands. However, modeling vegetation carbon sequestration of herbaceous wetlands is still problematic due to complex hydroecological processes and rapidly changing biomass carbon stocks. Theoretically, a vegetation index (VI) time series can retrieve the dynamic of biomass carbon stocks and could be used to calculate the cumulative composite of biomass carbon stocks during a given interval, i.e., vegetation carbon sequestration. Hence, we explored the potential for mapping vegetation carbon seques

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Kooistra, Lammert, Katja Berger, Benjamin Brede, et al. "Reviews and syntheses: Remotely sensed optical time series for monitoring vegetation productivity." Biogeosciences 21, no. 2 (2024): 473–511. http://dx.doi.org/10.5194/bg-21-473-2024.

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Abstract. Vegetation productivity is a critical indicator of global ecosystem health and is impacted by human activities and climate change. A wide range of optical sensing platforms, from ground-based to airborne and satellite, provide spatially continuous information on terrestrial vegetation status and functioning. As optical Earth observation (EO) data are usually routinely acquired, vegetation can be monitored repeatedly over time, reflecting seasonal vegetation patterns and trends in vegetation productivity metrics. Such metrics include gross primary productivity, net primary productivit

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Peterson, Abree A., Karen E. DeMatteo, Roger J. Michaelides, Stanton Braude, and Alan R. Templeton. "Time Series Analysis of Vegetation Recovery After the Taum Sauk Dam Failure." Remote Sensing 17, no. 9 (2025): 1605. https://doi.org/10.3390/rs17091605.

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On 14 December 2005, there was a catastrophic flood after a failure in the upper reservoir at the Taum Sauk Plant in southern Missouri. While there has been extensive research on the cause of the dam’s failure and the flood’s immediate impact, there has been limited investigation on how vegetation in and around the resulting flood scour has changed since this event. This study fills this gap through a time-series analysis using imagery sourced from GloVis and Planet Explorer to quantify vegetation levels prior to the flood (2005) through to 2024. Vegetation level was calculated using the Norma

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Landi, M. A., S. Ojeda, C. M. Di Bella, P. Salvatierra, J. P. Argañaraz, and L. M. Bellis. "Selección de parcelas control para estudios de la dinámica post-incendio: desempeño de rutinas no paramétricas y autorregresivas." Revista de Teledetección, no. 49 (December 5, 2017): 79. http://dx.doi.org/10.4995/raet.2017.7116.

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<p>Natural fire regimes have been modified; therefore robust post-fire monitoring tools are needed to understand the post-fire recovery process. Satellites with high temporal resolution allow us to build time series of vegetation indices for monitoring post-fire vegetation recovery. One of the techniques used is to compare the time series of a burned plot with that of an unburned control plot. However, for its implementation it is necessary to select control plots in which the vegetation has the same structure and functioning than the plot burned before the fire. Previous study defined b

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Maignan, F., F. M. Bréon, F. Chevallier, et al. "Evaluation of a Dynamic Global Vegetation Model using time series of satellite vegetation indices." Geoscientific Model Development Discussions 4, no. 2 (2011): 907–41. http://dx.doi.org/10.5194/gmdd-4-907-2011.

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Abstract. Atmospheric CO2 drives most of the greenhouse effect increase and one major uncertainty on the future rate of increase of CO2 in the atmosphere is the impact of the anticipated climate change on the vegetation. Dynamic Global Vegetation Models (DGVM) are used to address this question. ORCHIDEE is such a DGVM that has proven useful for climate change studies. However, there is no objective and methodological way to accurately assess each new available version on the global scale. In this paper, we submit a methodological evaluation of ORCHIDEE by correlating satellite-derived Vegetati

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Lu, Linlin, Claudia Kuenzer, Cuizhen Wang, Huadong Guo, and Qingting Li. "Evaluation of Three MODIS-Derived Vegetation Index Time Series for Dryland Vegetation Dynamics Monitoring." Remote Sensing 7, no. 6 (2015): 7597–614. http://dx.doi.org/10.3390/rs70607597.

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Yadav, S. K., and S. L. Borana. "MODIS DERIVED NDVI BASED TIME SERIES ANALYSIS OF VEGETATION IN THE JODHPUR AREA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W6 (July 26, 2019): 535–39. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w6-535-2019.

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<p><strong>Abstract.</strong> Arid region of India shows vast variation in climate and vegetation during last two decades. In order to analysis impact of monsoonal patterns on the vegetation indices of the arid zone, a three years (2015&amp;ndash;2017) temporal series Moderate Resolution Image Spectrometer (MODIS) data for Pre &amp; Post Monsoon was used for computing Normalized Difference Vegetation Index (NDVI). The cloud-free NDVI time series data are used to study the relationship between the rainfall pattern and the vegetation changes in Jodhpur District. ENVI an

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van Iersel, Wimala, Menno Straatsma, Hans Middelkoop, and Elisabeth Addink. "Multitemporal Classification of River Floodplain Vegetation Using Time Series of UAV Images." Remote Sensing 10, no. 7 (2018): 1144. http://dx.doi.org/10.3390/rs10071144.

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The functions of river floodplains often conflict spatially, for example, water conveyance during peak discharge and diverse riparian ecology. Such functions are often associated with floodplain vegetation. Frequent monitoring of floodplain land cover is necessary to capture the dynamics of this vegetation. However, low classification accuracies are found with existing methods, especially for relatively similar vegetation types, such as grassland and herbaceous vegetation. Unmanned aerial vehicle (UAV) imagery has great potential to improve the classification of these vegetation types owing to

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Morrison, K. A., and N. Thérien. "Release of Organic Carbon, Kjeldhal Nitrogen and Total Phosphorus from Flooded Vegetation." Water Quality Research Journal 31, no. 2 (1996): 305–18. http://dx.doi.org/10.2166/wqrj.1996.018.

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Abstract The release of organic carbon, Kjeldahl nitrogen and total phosphorus from submerged terrestrial vegetation was measured. The vegetation examined was alder, spruce, lichen and sphagnum moss. Samples of the vegetation were placed in 26-L tanks into which water was added. At different time intervals, water samples were withdrawn for analyses and replaced with pure water. The tanks were kept aerobic and at 18°C. Two experimental series were carried out for all vegetation species, the second series using less plant material. Most release occurred in the first three months for both series.

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Kalaryti, Theodora P., Nikolaos Th Fourniotis, and Efstratios E. Tzirtzilakis. "Numerical Study of Turbulent Open-Channel Flow Through Submerged Rigid Vegetation." Water 17, no. 14 (2025): 2156. https://doi.org/10.3390/w17142156.

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In the present study, three-dimensional turbulent, subcritical open-channel flow (Fr = 0.19) through submerged rigid vegetation is numerically investigated using the ANSYS FLUENT solver (v. 22. 1). The Volume of Fluid (VOF) method is applied for free-surface tracking, while the standard k-ε turbulence model is employed for turbulence closure. Vegetation is modeled as vertical rigid cylinders fixed at the bottom of the channel. Regarding the arrangement of the stems, two cases are examined. In the first case, a linear arrangement of three equally spaced vegetative stems is located transversely

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Økland, R. H., and E. Bendiksen. "The vegetation of the forest-alpine transition in the Grunningsdalen area, Telemark, S. Norway." Sommerfeltia 2, no. 1 (1985): 1–171. http://dx.doi.org/10.2478/som-1985-0002.

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Abstract This study is divided into two parts. The general part contains a review of theories of the nature of vegetation. It is concluded that present evidence points in the direction of species individuality and vegetational continuum as responses to continuous environmental gradients, on a regional, and mostly also on a local scale. Accordingly, a gradient approach to classification of the forest and alpine vegetation of the investigated area was designed as an alternative to traditional phytosociological classification. The importance of the concept of corresponding vegetation types in dif

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Bueno, Inacio T., Greg J. McDermid, Eduarda M. O. Silveira, Jennifer N. Hird, Breno I. Domingos, and Fausto W. Acerbi Júnior. "Spatial Agreement among Vegetation Disturbance Maps in Tropical Domains Using Landsat Time Series." Remote Sensing 12, no. 18 (2020): 2948. http://dx.doi.org/10.3390/rs12182948.

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Detecting disturbances in native vegetation is a crucial component of many environmental management strategies, and remote sensing-based methods are the most efficient way to collect multi-temporal disturbance data over large areas. Given that there is a large range of datasets for monitoring, analyzing, and detecting disturbances, many methods have been well-studied and successfully implemented. However, factors such as the vegetation type, input data, and change detection method can significantly alter the outcomes of a disturbance-detection study. We evaluated the spatial agreement of distu

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Ba, Rui, Michele Lovallo, Weiguo Song, Hui Zhang, and Luciano Telesca. "Multifractal Analysis of MODIS Aqua and Terra Satellite Time Series of Normalized Difference Vegetation Index and Enhanced Vegetation Index of Sites Affected by Wildfires." Entropy 24, no. 12 (2022): 1748. http://dx.doi.org/10.3390/e24121748.

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The MODIS Aqua and Terra Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) time series acquired during nearly two decades (2000 to 2020) covering the area burned by the Camp Fire (California) in 2018 is investigated in this study by using the multifractal detrended fluctuation analysis in relation to the recovery process of vegetation after fire. In 2008, the same area was partially burned by two wildfires, the BTU Lightning Complex Fire and the Humboldt Fire. Our results indicate that all vegetation index time series are featured by six- and twelve-month modula

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Jia, L., H. Shang, G. Hu, and M. Menenti. "Phenological response of vegetation to upstream river flow in the Heihe Rive basin by time series analysis of MODIS data." Hydrology and Earth System Sciences 15, no. 3 (2011): 1047–64. http://dx.doi.org/10.5194/hess-15-1047-2011.

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Abstract. Liquid and solid precipitation is abundant in the high elevation, upper reach of the Heihe River basin in northwestern China. The development of modern irrigation schemes in the middle reach of the basin is taking up an increasing share of fresh water resources, endangering the oasis and traditional irrigation systems in the lower reach. In this study, the response of vegetation in the Ejina Oasis in the lower reach of the Heihe River to the water yield of the upper catchment was analyzed by time series analysis of monthly observations of precipitation in the upper and lower catchmen

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Makarova, M. A. "Large-scale vegetation mapping of the Pinega river valley (the surroundings of Golubino village, Arkhangelsk oblast)." Geobotanical mapping, no. 2018 (2018): 19–39. http://dx.doi.org/10.31111/geobotmap/2018.19.

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Geobotanical survey of floodplain natural complexes near gypsum outcrops in the Pinega river valley was done in 2015. Large-scale geobotanical map of the key polygon (scale 1 : 30 000) was composed. Typological units of vegetation were selected on the basis of the composition of dominant species and groups of indicator species. Homogeneous and heterogeneous territorial units of vegetation (serial series, combinations, environmental series) were used. 53 mapped unit types (25 homogeneous types and 28 heterogeneous types) were recognized. The floodplain vegetation consists of 17 homogeneous type

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Sha, Zong Yao, and Yong Fei Bai. "Building Long-Term and Consistent Vegetation Index Based on Association Analysis between Different VI Products." Advanced Materials Research 518-523 (May 2012): 5261–66. http://dx.doi.org/10.4028/www.scientific.net/amr.518-523.5261.

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The dynamics of vegetation cover plays an important role in global environment evaluation. Due to the spatial, spectral and radiometric differences among different remote sensing platforms, building long-term and consistent vegetation index (VI) time series is desired to derive comparable vegetation health. In this paper, an approach called Min_Max stretch transformation (MMST) was proposed to generate long-term and consistent VI series based on spatio-temporal association analysis between AVHRR NDVI and MODIS EVI. The proposed approach intended to map AVHRR NDVI to MODIS EVI level and thus bo

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Santana, Níckolas Castro, Osmar Abílio de Carvalho Júnior, Roberto Arnaldo Trancoso Gomes, and Renato Fontes Guimarães. "Comparison of Post-fire Patterns in Brazilian Savanna and Tropical Forest from Remote Sensing Time Series." ISPRS International Journal of Geo-Information 9, no. 11 (2020): 659. http://dx.doi.org/10.3390/ijgi9110659.

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Monitoring of fire-related changes is essential to understand vegetation dynamics in the medium and long term. Remote sensing time series allows estimating biophysical variables of terrestrial vegetation and interference by extreme fires. This research evaluated fire recurrence in the Amazon and Cerrado regions, using Moderate Resolution Imaging Spectroradiometer (MODIS) albedo time series, enhanced vegetation index (EVI), gross primary productivity (GPP), and surface temperature. The annual aggregated time series (AAT) method recognized each pixel’s slope trend in the 2001–2016 period and its

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van Leeuwen, Willem J. D., Grant M. Casady, Daniel G. Neary, et al. "Monitoring post-wildfire vegetation response with remotely sensed time-series data in Spain, USA and Israel." International Journal of Wildland Fire 19, no. 1 (2010): 75. http://dx.doi.org/10.1071/wf08078.

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Due to the challenges faced by resource managers in maintaining post-fire ecosystem health, there is a need for methods to assess the ecological consequences of disturbances. This research examines an approach for assessing changes in post-fire vegetation dynamics for sites in Spain, Israel and the USA that burned in 1998, 1999 and 2002 respectively. Moderate Resolution Imaging Spectroradiometer satellite Normalized Difference Vegetation Index (NDVI) time-series data (2000–07) are used for all sites to characterise and track the seasonal and spatial changes in vegetation response. Post-fire tr

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Zhou, Qu, Xianghan Sun, Liqiao Tian, Jian Li, and Wenkai Li. "Grouping-Based Time-Series Model for Monitoring of Fall Peak Coloration Dates Using Satellite Remote Sensing Data." Remote Sensing 12, no. 2 (2020): 274. http://dx.doi.org/10.3390/rs12020274.

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Accurate monitoring of plant phenology is vital to effective understanding and prediction of the response of vegetation ecosystems to climate change. Satellite remote sensing is extensively employed to monitor vegetation phenology. However, fall phenology, such as peak foliage coloration, is less well understood compared with spring phenological events, and is mainly determined using the vegetation index (VI) time-series. Each VI only emphasizes a single vegetation property. Thus, selecting suitable VIs and taking advantage of multiple spectral signatures to detect phenological events is chall

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