Articoli di riviste sul tema "Vegetation series"
Segui questo link per vedere altri tipi di pubblicazioni sul tema: Vegetation series.
Cita una fonte nei formati APA, MLA, Chicago, Harvard e in molti altri stili
Vedi i top-50 articoli di riviste per l'attività di ricerca sul tema "Vegetation series".
Accanto a ogni fonte nell'elenco di riferimenti c'è un pulsante "Aggiungi alla bibliografia". Premilo e genereremo automaticamente la citazione bibliografica dell'opera scelta nello stile citazionale di cui hai bisogno: APA, MLA, Harvard, Chicago, Vancouver ecc.
Puoi anche scaricare il testo completo della pubblicazione scientifica nel formato .pdf e leggere online l'abstract (il sommario) dell'opera se è presente nei metadati.
Vedi gli articoli di riviste di molte aree scientifiche e compila una bibliografia corretta.
1
Simoniello, T., M. Lanfredi, M. Liberti, R. Coppola e M. Macchiato. "Estimation of vegetation cover resilience from satellite time series". Hydrology and Earth System Sciences Discussions 5, n. 1 (28 febbraio 2008): 511–46. http://dx.doi.org/10.5194/hessd-5-511-2008.
Abstract (sommario):
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 activity. In order to characterize resilience, we analyzed the time series 1982–2003 of 8 km GIMMS AVHRR-NDVI maps of the Italian territory. Persistence probability of negative and positive trends was estimated according to the vegetation cover class, altitude, and climate. Generally, mean recovery times from negative trends were shorter than those estimated for positive trends, as expected for vegetation of healthy status. Some signatures of inefficient resilience were found in high-level mountainous areas and in the Mediterranean sub-tropical ones. This analysis was refined by aggregating pixels according to phenology. This multitemporal clustering synthesized information on vegetation cover, climate, and orography rather well. The consequent persistence estimations confirmed and detailed hints obtained from the previous analyses. Under the same climatic regime, different vegetation resilience levels were found. In particular, within the Mediterranean sub-tropical climate, clustering was able to identify features with different persistence levels in areas that are liable to different levels of anthropic pressure. Moreover, it was capable of enhancing reduced vegetation resilience also in the southern areas under Warm Temperate sub-continental climate. The general consistency of the obtained results showed that, with the help of suited analysis methodologies, 8 km AVHRR-NDVI data could be useful for capturing details on vegetation cover activity at local scale even in complex territories such as that of the Italian peninsula.
2
Simoniello, T., M. Lanfredi, M. Liberti, R. Coppola e M. Macchiato. "Estimation of vegetation cover resilience from satellite time series". Hydrology and Earth System Sciences 12, n. 4 (30 luglio 2008): 1053–64. http://dx.doi.org/10.5194/hess-12-1053-2008.
Abstract (sommario):
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 activity. In order to characterize resilience, we analyzed the time series 1982–2003 of 8 km GIMMS AVHRR-NDVI maps of the Italian territory. Persistence probability of negative and positive trends was estimated according to the vegetation cover class, altitude, and climate. Generally, mean recovery times from negative trends were shorter than those estimated for positive trends, as expected for vegetation of healthy status. Some signatures of inefficient resilience were found in high-level mountainous areas and in the Mediterranean sub-tropical ones. This analysis was refined by aggregating pixels according to phenology. This multitemporal clustering synthesized information on vegetation cover, climate, and orography rather well. The consequent persistence estimations confirmed and detailed hints obtained from the previous analyses. Under the same climatic regime, different vegetation resilience levels were found. In particular, within the Mediterranean sub-tropical climate, clustering was able to identify features with different persistence levels in areas that are liable to different levels of anthropic pressure. Moreover, it was capable of enhancing reduced vegetation resilience also in the southern areas under Warm Temperate sub-continental climate. The general consistency of the obtained results showed that, with the help of suited analysis methodologies, 8 km AVHRR-NDVI data could be useful for capturing details on vegetation cover activity at local scale even in complex territories such as that of the Italian peninsula.
3
Khan, Asim, Warda Asim, Anwaar Ulhaq e Randall W. Robinson. "A deep semantic vegetation health monitoring platform for citizen science imaging data". PLOS ONE 17, n. 7 (27 luglio 2022): e0270625. http://dx.doi.org/10.1371/journal.pone.0270625.
Abstract (sommario):
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 science project. In this article, we build upon our previous work on developing a Semantic Vegetation Index (SVI) and expand it to introduce a semantic vegetation health monitoring platform to monitor vegetation health in a large landscape. However, unlike our previous work, we use RGB images of the Australian landscape for a quarterly series of images over six years (2015–2020). This Semantic Vegetation Index (SVI) is based on deep semantic segmentation to integrate it with a citizen science project (Fluker Post) for automated environmental monitoring. It has collected thousands of vegetation images shared by various visitors from around 168 different points located in Australian regions over six years. This paper first uses a deep learning-based semantic segmentation model to classify vegetation in repeated photographs. A semantic vegetation index is then calculated and plotted in a time series to reflect seasonal variations and environmental impacts. The results show variational trends of vegetation cover for each year, and the semantic segmentation model performed well in calculating vegetation cover based on semantic pixels (overall accuracy = 97.7%). This work has solved a number of problems related to changes in viewpoint, scale, zoom, and seasonal changes in order to normalise RGB image data collected from different image devices.
4
Dobremez, J. F. "Vegetation classification and vegetation mapping in the Himalayas". Geobotanical mapping, n. 1994-1995 (1996): 45–50. http://dx.doi.org/10.31111/geobotmap/1994-1995.45.
Abstract (sommario):
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 basic concepts of vegetation level and vegetation series by Gaussen and Ozenda along with the biogeographic concept taking into account the diversity of flora and vegetation. The concept of vegetation level reflects the altitudinal zonation of vegetation, 11 vegetation levels being distinguished in Central Himalayas. The other basic concept is that of vegetation series depicting the dynamics of vegetation cover with respect to human activity. One series includes all the vegetation types which terminate, by natural evolution, in one climax vegetation type (potential vegetation). For Nepal about 100 vegetation series have been described and mapped.
5
Patel, J. H., e M. P. Oza. "Deriving crop calendar using NDVI time-series". ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-8 (28 novembre 2014): 869–73. http://dx.doi.org/10.5194/isprsarchives-xl-8-869-2014.
Abstract (sommario):
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 measured by the Normalized Difference Vegetation Index (NDVI) and its profiles, can provide a new dimension for describing vegetation growth cycle. The analysis based on values of NDVI at regular time interval provides useful information about various crop growth stages and performance of crop in a season. However, the NDVI data series has considerable amount of local fluctuation in time domain and needs to be smoothed so that dominant seasonal behavior is enhanced. Based on temporal analysis of smoothed NDVI series, it is possible to extract number of crop cycles per year and their crop calendar. <br><br> In the present study, a methodology is developed to extract key elements of crop growth cycle (i.e. number of crops per year and their planting – peak - harvesting dates). This is illustrated by analysing MODIS-NDVI data series of one agricultural year (from June 2012 to May 2013) over Gujarat. Such an analysis is very useful for analysing dynamics of kharif and rabi crops.
6
Rivas-Martínez, S., e D. Sánchez-Mata. "Boreal vegetation series of North America". Plant Biosystems - An International Journal Dealing with all Aspects of Plant Biology 145, sup1 (settembre 2011): 208–19. http://dx.doi.org/10.1080/11263504.2011.602742.
7
Liu, Gui Xiang, Zhuo Yi, Feng Ming Yu e Chun Long Jiang. "Study on Effect of Drought Based on Time Series on Grassland Vegetation in Eastern Inner Mongolia". Advanced Materials Research 518-523 (maggio 2012): 5306–15. http://dx.doi.org/10.4028/www.scientific.net/amr.518-523.5306.
Abstract (sommario):
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 period, but relatively not that sensitive in the budding and maturation period, and that, the vegetations in meadow grassland, typical grassland and desert grassland vary in the responses to the drought.
8
Sun, Chao, Jialin Li, Luodan Cao, Yongchao Liu, Song Jin e Bingxue Zhao. "Evaluation of Vegetation Index-Based Curve Fitting Models for Accurate Classification of Salt Marsh Vegetation Using Sentinel-2 Time-Series". Sensors 20, n. 19 (28 settembre 2020): 5551. http://dx.doi.org/10.3390/s20195551.
Abstract (sommario):
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 series, namely Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), Enhanced Vegetation Index (EVI), Green Normalized Difference Vegetation Index (GNDVI), and Water-Adjusted Vegetation Index (WAVI), was compared empirically; the suitability between three curve fitting methods, namely Asymmetric Gaussian (AG), Double Logistic (DL), and Two-term Fourier (TF), and VI time series was measured using the coefficient of determination, and the salt marsh vegetation separability among different combinations of VI time series and curve fitting methods (i.e., VI time series-based curve fitting model) was quantified using overall the Jeffries–Matusita distance. Six common types of salt marsh vegetation from three typical coastal sites in China were used to validate these findings, which demonstrate: (1) the SAVI performed best in terms of time series stability, while the EVI exhibited relatively poor time series stability with conspicuous outliers induced by the sensitivity to omitted clouds and shadows; (2) the DL method commonly resulted in the most accurate classification of different salt marsh vegetation types, especially when combined with the EVI time series, followed by the TF method; and (3) the SAVI/NDVI-based DL/TF model demonstrated comparable efficiency for classifying salt marsh vegetation. Notably, the SAVI/NDVI-based DL model performed most strongly for high latitude regions with a continental climate, whilst the SAVI/NDVI-based TF model appears to be better suited to mid- to low latitude regions dominated by a monsoonal climate.
9
Osipov, S. V., e 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, n. 1998-2000 (2000): 50–61. http://dx.doi.org/10.31111/geobotmap/1998-2000.50.
Abstract (sommario):
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 supracoenotic level). Such categories, as vegetation of tops and slopes, lowlands and river valleys, sea coasts reflect maximal contrasts in vegetation cover, so they are the highest level divisions of the map legend. Types of succession series and stages of series are developed for construction of the second and third levels of the legend. Communities, similar in ecotope, total species composition, saplings and some other characteristics, are referred to one type of series. 5 types of series have been distinguished: dry, fresh, moist, very moist, wet. The main factor of dynamics in considered territory is fire and the series are mainly pyrogeneous. Series are presented as sequences of vegetation stages. The vegetation stages for tops and slopes are: closed low forest — open low woodland — shrub thicket with saplings — meadow with saplings, for lowlands and river valleys they are: open low woodland — thicket of saplings — meadow or mire with saplings.
10
Najafi, Z., P. Fatehi e 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 (18 ottobre 2019): 783–88. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w18-783-2019.
Abstract (sommario):
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 observed for the first half or second half of April. Trend analysis using regression and Theil-Sen methods indicated a no-trend in vegetation fractions. The pixel-based trend assessment using regression showed that a 50% of the study area faced a positive trend and reaming part faced a negative trend. The Theil-Sen method revealed the no-trend for a large majority of area. The Mann-Kendall test indicated that only 20 percent of the area shows a statistically significant trend.
11
Yu, Rong, Bo Feng Cai, Xiang Qin Su, Ya Zi He e 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 (giugno 2014): 863–68. http://dx.doi.org/10.4028/www.scientific.net/amr.955-959.863.
Abstract (sommario):
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 the six kinds of main land covers’ vegetation index time series data of Northeast China region.
12
TELESCA, LUCIANO, ROSA LASAPONARA e ANTONIO LANORTE. "DISCRIMINATING FLUCTUATION DYNAMICS IN BURNED AND UNBURNED VEGETATIONAL COVERS". Fluctuation and Noise Letters 05, n. 04 (dicembre 2005): L479—L487. http://dx.doi.org/10.1142/s0219477505002914.
Abstract (sommario):
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 evolution of the shrub-land, increasing the persistence of the vegetation dynamics.
13
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, n. 3 (31 marzo 2023): 1453–60. http://dx.doi.org/10.22214/ijraset.2023.49693.
Abstract (sommario):
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 of data sets, satellite remotely sensed data can be effectively used to characterize land surface conditions and land surface changes.
14
Liu, Yu, Wenqing Li, Li Li e Naiqun Zhang. "Extraction of Long Time-Series Vegetation Indices from Combined Multisource Satellite Imagery". Computational Intelligence and Neuroscience 2022 (30 maggio 2022): 1–8. http://dx.doi.org/10.1155/2022/3901372.
Abstract (sommario):
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 extract NDVI and RVI at the corresponding locations. Linear regression and Spearman correlation coefficient (ρ) analysis were used to verify the correlation between NDVI and RVI from multisource images. The results showed that the vegetation indices fluctuated smoothly in the time series within the validation sites, and the vegetation indices of multisource satellite images were good measures of long-term vegetation cover in the region; the vegetation indices of the same satellite images showed significant correlations (both R2 and ρ exceeded 0.8), and the vegetation indices of different satellite images (PSM and MSI, PSM and OLI) showed more significant correlations (both R2 and ρ exceeded 0.7); the smaller the difference between the original resolutions of satellite images, the more significant the correlation between the extracted NDVI and RVI.
15
Priya, M. V., R. Kalpana, S. Pazhanivelan, R. Kumaraperumal, K. P. Ragunath, G. Vanitha, Ashmitha Nihar, P. J. Prajesh e Vasumathi V. "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, n. 3 (19 settembre 2023): 1170–77. http://dx.doi.org/10.31018/jans.v15i3.4803.
Abstract (sommario):
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.
16
Maignan, F., F. M. Bréon, F. Chevallier, N. Viovy, P. Ciais, C. Garrec, J. Trules e M. Mancip. "Evaluation of a Global Vegetation Model using time series of satellite vegetation indices". Geoscientific Model Development 4, n. 4 (5 dicembre 2011): 1103–14. http://dx.doi.org/10.5194/gmd-4-1103-2011.
Abstract (sommario):
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 Index time series against those of the modeled Fraction of absorbed Photosynthetically Active Radiation (FPAR). A perfect correlation between the two is not expected, however an improvement of the model should lead to an increase of the overall performance. We detail two case studies in which model improvements are demonstrated, using our methodology. In the first one, a new phenology version in ORCHIDEE is shown to bring a significant impact on the simulated annual cycles, in particular for C3 Grasses and C3 Crops. In the second case study, we compare the simulations when using two different weather fields to drive ORCHIDEE. The ERA-Interim forcing leads to a better description of the FPAR interannual anomalies than the simulation forced by a mixed CRU-NCEP dataset. This work shows that long time series of satellite observations, despite their uncertainties, can identify weaknesses in global vegetation models, a necessary first step to improving them.
17
Bellone, Tamara, Piero Boccardo e Francesca Perez. "Investigation of Vegetation Dynamics using Long-Term Normalized Difference Vegetation Index Time-Series". American Journal of Environmental Sciences 5, n. 4 (1 aprile 2009): 460–66. http://dx.doi.org/10.3844/ajessp.2009.460.466.
18
Bellone. "Investigation of Vegetation Dynamics using Long-Term Normalized Difference Vegetation Index Time-Series". American Journal of Environmental Sciences 5, n. 4 (1 aprile 2009): 461–67. http://dx.doi.org/10.3844/ajessp.2009.461.467.
19
LI, Yunqing, Kazuhiko OHNUMA e 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, n. 1 (1990): 4–12. http://dx.doi.org/10.4287/jsprs.29.4.
20
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.
Abstract (sommario):
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 diversity, ecological plant response, and invasion indices. All treatments were successful at improving soil fertility and promoting a self-sustaining vegetative cover. The level of available nitrogen had a strong impact on vegetative coverage, species distribution, and extent of unseeded vegetation. For example, high nitrogen treatments promoted a grass-dominated (low forb) plant community with a low content of unseeded vegetation. In contrast, low nitrogen treatments promoted a more balanced plant community with a mixture of grass and forb species and greater susceptibility to unseeded vegetation establishment.
21
Lan, Shengxin, e Zuoji Dong. "Incorporating Vegetation Type Transformation with NDVI Time-Series to Study the Vegetation Dynamics in Xinjiang". Sustainability 14, n. 1 (5 gennaio 2022): 582. http://dx.doi.org/10.3390/su14010582.
Abstract (sommario):
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 the vegetation showed no significant changes. In the vegetation-changed area, approximately 93.88% and 6.12% of the vegetation showed a significant increase and decrease in NDVI, respectively. Approximately 43,382.82 km2 of sparse vegetation and 25,915.44 km2 of grassland were transformed into grassland and cropland, respectively. Moreover, 17.4% of the area with transformed vegetation showed a significant increase in NDVI, whereas 14.61% showed a decrease in NDVI. Furthermore, in areas with NDVI increased, the mean NDVI slopes of pixels in which sparse vegetation transferred to cropland, sparse vegetation transferred to grassland, and grassland transferred to cropland were 9.8 and 3.2 times that of sparse vegetation, and 1.97 times that of grassland, respectively. In areas with decreased NDVI, the mean NDVI slopes of pixels in which cropland transferred to sparse vegetation, grassland transferred to sparse vegetation were 1.75 and 1.36 times that of sparse vegetation, respectively. The combination of vegetation type transformation NDVI time-series can assist in comprehensively understanding the vegetation change characteristics.
22
Liu, Hualiang, Feizhou Zhang, Lifu Zhang, Yukun Lin, Siheng Wang e Yefeng Xie. "UNVI-Based Time Series for Vegetation Discrimination Using Separability Analysis and Random Forest Classification". Remote Sensing 12, n. 3 (6 febbraio 2020): 529. http://dx.doi.org/10.3390/rs12030529.
Abstract (sommario):
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 with all spectral bands incorporated, has been proved to be more effective than several commonly used satellite-derived VIs in some application scenarios. In this study, we explored the ability of UNVI time series for discriminating different vegetation types in Chaoyang prefecture, northeast China, in comparison with normalized NDVI, EVI, triangle vegetation index (TVI), and tasseled cap transformation greenness (TCG). These five indices were calculated using Landsat 8 surface reflectance data, and two comparative experiments were conducted. The first experiment analyzed class separabilities using pairwise JM (Jeffries–Matusita) distance as indicator, and the results showed that UNVI was superior to EVI, TVI, and TCG, and almost equivalent to NDVI, especially during the peak of vegetation growing season and for the most indistinguishable vegetation pair broadleaf and shrubs. The second experiment compared the vegetation classification accuracies using the features of these VI temporal profiles and the corresponding phenological parameters, and the results showed that UNVI can better classify the five major vegetation in Chaoyang prefecture than other four indices. Therefore, we conclude that UNVI time series has considerable potential for regional land cover mapping, and we recommend that the use of the UNVI is considered in the future time series related studies.
23
Zibzeev, E. G. "High-mountain vegetation of the southeastern part of Tigiretsk Ridge (West Altai)". Vegetation of Russia, n. 6 (2004): 23–34. http://dx.doi.org/10.31111/vegrus/2004.06.23.
Abstract (sommario):
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.
24
Kalibernova, N. M. "Showing the vegetation cover of flood-plains and river valleys on the Vegetation Map of Kazakhstan and middle Asia". Geobotanical mapping, n. 1993 (1995): 58–66. http://dx.doi.org/10.31111/geobotmap/1993.58.
Abstract (sommario):
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 corresponding plant communities manifest themselves first of all. Mineralization of ground waters, salinity of soils, including the alluvial ones, are of essential importance. The practice of vegetation mapping of unstable habitats, to which floodplain landscapes belong, has shown that units of phytocoenological classification is of little use for this purpose. The heterogeneity of vegetation, consisting of short-term unstable serial communities generates a need for typification of space combinations of such phytocoenoses. For this purpose it is convenient to use generalized ecological-dinamic series, including plant communities of all levels within the limits of definite segment of valley. These series are the mapping units on the map. The vegetation of the first terrace is also nessecary to include in a single series with flood-plain vegetation because it has supplementary influence of ground waters. The higher divisions of the legend are based on zonal characters: vegetation of valleys in northern, middle and southern deserts. 13 numbers are used to show the vegetation cover of flood-plains and valleys. Additional 7 numbers are used for the out-of-valley meadow vegetation. The content is enriched by using of the letters by the numbers showing the geographic variants of series and ciphers for combination of series and out-of-series communities. The text legend is supplemented by the matrix (table), showing the subordination of subtitles, zonal position and geographic distribution of divisions. The types of series in the matrix are listed with indication of the main dominant species that gives the additional information on the legend divisions. The author's conclusion is that valley vegetation reveals clearly the zonal features, correlating with zonal (desert) vegetation.
25
León-Tavares, Jonathan, Jean-Louis Roujean, Bruno Smets, Erwin Wolters, Carolien Toté e Else Swinnen. "Correction of Directional Effects in VEGETATION NDVI Time-Series". Remote Sensing 13, n. 6 (16 marzo 2021): 1130. http://dx.doi.org/10.3390/rs13061130.
Abstract (sommario):
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 directional effects is not a real issue provided that they can be properly removed, which supposes an appropriate BRDF (Bidirectional Reflectance Distribution Function) sampling as offered by the VEGETATION program. An anisotropy correction supports a better analysis of the temporal shapes and spatial patterns of land surface reflectance values and vegetation indices such as NDVI. Herein we describe a BRDF correction methodology, for the purpose of the Copernicus Global Land Service framework, which includes notably an adaptive data accumulation window and provides uncertainties associated with the NDVI computed with normalized reflectance. Assessing the general performance of the methodology in comparing time-series between normalized and directional NDVI reveals a significant removal of the high-frequency noise due to directional effects. The proposed methodology is computationally efficient to operate at a global scale to deliver a BRDF-corrected NDVI product based on long-term Time-Series of VEGETATION sensor and its follow-on with the Copernicus Sentinel-3 satellite constellation.
26
Li, Huayu, Jianhua Wan, Shanwei Liu, Hui Sheng e Mingming Xu. "Wetland Vegetation Classification through Multi-Dimensional Feature Time Series Remote Sensing Images Using Mahalanobis Distance-Based Dynamic Time Warping". Remote Sensing 14, n. 3 (21 gennaio 2022): 501. http://dx.doi.org/10.3390/rs14030501.
Abstract (sommario):
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 series was employed in this research. This method extends the traditional DTW algorithm based on single-dimensional features to multi-dimensional features and solves the problem of calculating similarity distance between multi-dimensional feature time series. Vegetation classification experiments were carried out in the Yellow River Delta (YRD). Compared with different classification methods, the results show that the K-Nearest Neighbors (KNN) algorithm based on MDDTW (KNN-MDDTW) has achieved better classification accuracy; the overall accuracy is more than 90%, and kappa is more than 0.9.
27
Khosravirad, M., M. Omid, F. Sarmadian e 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 (18 ottobre 2019): 645–48. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w18-645-2019.
Abstract (sommario):
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 values of vegetation indices related to first and second periods of growth (from 2004 to 2016 extracted from time-series data) were evaluated by simple linear regression models against the average observed yields efficiency. The result showed the accumulative IAVI (γ = 1.4) vegetation index relative to first period of growth with R2 = 0.66 and RMSE = 3.78 ton/ha and the accumulative NDI vegetation index relative to second period of growth with R2 = 0.66 and RMSE = 3.79 ton/ha and the accumulative NDI vegetation index relative to sum of the first and the second growth periods with R2 = 0.78 and RMSE = 3.09 ton/ha had good agreement with sugarcane stem yield efficiency at the middle of growth and before harvesting season.
28
Kooistra, Lammert, Katja Berger, Benjamin Brede, Lukas Valentin Graf, Helge Aasen, Jean-Louis Roujean, Miriam Machwitz et al. "Reviews and syntheses: Remotely sensed optical time series for monitoring vegetation productivity". Biogeosciences 21, n. 2 (25 gennaio 2024): 473–511. http://dx.doi.org/10.5194/bg-21-473-2024.
Abstract (sommario):
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 productivity, biomass, or yield. To summarize current knowledge, in this paper we systematically reviewed time series (TS) literature for assessing state-of-the-art vegetation productivity monitoring approaches for different ecosystems based on optical remote sensing (RS) data. As the integration of solar-induced fluorescence (SIF) data in vegetation productivity processing chains has emerged as a promising source, we also include this relatively recent sensor modality. We define three methodological categories to derive productivity metrics from remotely sensed TS of vegetation indices or quantitative traits: (i) trend analysis and anomaly detection, (ii) land surface phenology, and (iii) integration and assimilation of TS-derived metrics into statistical and process-based dynamic vegetation models (DVMs). Although the majority of used TS data streams originate from data acquired from satellite platforms, TS data from aircraft and unoccupied aerial vehicles have found their way into productivity monitoring studies. To facilitate processing, we provide a list of common toolboxes for inferring productivity metrics and information from TS data. We further discuss validation strategies of the RS data derived productivity metrics: (1) using in situ measured data, such as yield; (2) sensor networks of distinct sensors, including spectroradiometers, flux towers, or phenological cameras; and (3) inter-comparison of different productivity metrics. Finally, we address current challenges and propose a conceptual framework for productivity metrics derivation, including fully integrated DVMs and radiative transfer models here labelled as “Digital Twin”. This novel framework meets the requirements of multiple ecosystems and enables both an improved understanding of vegetation temporal dynamics in response to climate and environmental drivers and enhances the accuracy of vegetation productivity monitoring.
29
Dai, Xue, Guishan Yang, Desheng Liu e 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, n. 18 (15 settembre 2020): 3000. http://dx.doi.org/10.3390/rs12183000.
Abstract (sommario):
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 sequestration in herbaceous wetlands in this study by using a combination of remotely sensed VI time series and field observation data. This method was exemplarily applied for Poyang Lake wetland in 2016 by using a 16-day Moderate Resolution Imaging Spectroradiometer (MODIS) enhanced vegetation index (EVI) time series. Results show that the vegetation carbon sequestration in this area was in the range of 193–1221 g C m−2 year−1 with a mean of 401 g C m−2 year−1 and a standard deviation of 172 g C m−2 year−1 in 2016. The approach has wider spatial applicability in wetlands than the currently used global map of vegetation production (MOD17A3) because our carbon estimation in areas depicted by ‘no data’ in the MOD17A3 product is considerable, which accounts for 91.2–91.5% of the total vegetation carbon sequestration of the wetland. Thus, we determined that VI time series data shows great potential for estimating vegetation carbon sequestration in herbaceous wetlands, especially with the continuously improving quality and frequency of satellite VI images.
30
Maignan, F., F. M. Bréon, F. Chevallier, N. Viovy, P. Ciais, C. Garrec, J. Trules e M. Mancip. "Evaluation of a Dynamic Global Vegetation Model using time series of satellite vegetation indices". Geoscientific Model Development Discussions 4, n. 2 (29 aprile 2011): 907–41. http://dx.doi.org/10.5194/gmdd-4-907-2011.
Abstract (sommario):
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 Vegetation Index time series against those of the modeled Fraction of absorbed Photosynthetically Active Radiation (FPAR). A perfect correlation between the two is not expected, however an improvement of the model should lead to an increase of the median correlation. We detail two case studies in which model improvements are demonstrated, using our methodology. In the first one, a new phenology version in ORCHIDEE is shown to bring a significant impact on the simulated annual cycles, in particular for C3 Grasses and C3 Crops. In the second case study, we compare the simulations when using two different weather fields to drive ORCHIDEE. The ERA-Interim forcing leads to a better description of the FPAR interannual anomalies than the simulation forced by a mixed CRU-NCEP dataset. This work shows that long time series of satellite observations, despite their uncertainties, can identify weaknesses in global vegetation models, a necessary first step to improving them.
31
Lu, Linlin, Claudia Kuenzer, Cuizhen Wang, Huadong Guo e Qingting Li. "Evaluation of Three MODIS-Derived Vegetation Index Time Series for Dryland Vegetation Dynamics Monitoring". Remote Sensing 7, n. 6 (9 giugno 2015): 7597–614. http://dx.doi.org/10.3390/rs70607597.
32
van Iersel, Wimala, Menno Straatsma, Hans Middelkoop e Elisabeth Addink. "Multitemporal Classification of River Floodplain Vegetation Using Time Series of UAV Images". Remote Sensing 10, n. 7 (19 luglio 2018): 1144. http://dx.doi.org/10.3390/rs10071144.
Abstract (sommario):
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 its high spatial resolution and flexibility in image acquisition timing. This study aimed to evaluate the increase in classification accuracy obtained using multitemporal UAV images versus single time step data on floodplain land cover classification and to assess the effect of varying the number and timing of imagery acquisition moments. We obtained a dataset of multitemporal UAV imagery and field reference observations and applied object-based Random Forest classification (RF) to data of different time step combinations. High overall accuracies (OA) exceeding 90% were found for the RF of floodplain land cover, with six vegetation classes and four non-vegetation classes. Using two or more time steps compared with a single time step increased the OA from 96.9% to 99.3%. The user’s accuracies of the classes with large similarity, such as natural grassland and herbaceous vegetation, also exceeded 90%. The combination of imagery from June and September resulted in the highest OA (98%) for two time steps. Our method is a practical and highly accurate solution for monitoring areas of a few square kilometres. For large-scale monitoring of floodplains, the same method can be used, but with data from airborne platforms covering larger extents.
33
Landi, M. A., S. Ojeda, C. M. Di Bella, P. Salvatierra, J. P. Argañaraz e L. M. Bellis. "Selección de parcelas control para estudios de la dinámica post-incendio: desempeño de rutinas no paramétricas y autorregresivas". Revista de Teledetección, n. 49 (5 dicembre 2017): 79. http://dx.doi.org/10.4995/raet.2017.7116.
Abstract (sommario):
<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 biological criteria to detect burned and unburned control plots with identical pre-fire vegetation functioning. Moreover, a non-parametric test routine of low statistical power was proposed to test them, this was based on the analysis of the QVI (Quotient Vegetation Index), calculated between NDVI (Normalized Difference Vegetation Index) time series of the burned and control site. However, currently there are autoregressive analysis techniques with greater statistical power. Therefore the aims were to propose six new statistical routines based on autoregressive test, and compare the performance of these with the non-parametric routine. We selected 13,700 forest plots and extracted the NDVI MODIS time series between 2002 and 2005. We randomly selected 43 reference plots, and through each routine, we compared each reference time series with the other 13,657 time series. We estimated the performance of the routines measuring the euclidian distance between the time series of the reference plot and the time series of the plots accepted for each routine. We also measured the quality and the amount of the QVI time series selected by each routine. Autoregressive routines showed better performance than the non-parametric routine, since they selected control plots with NDVI time series with greatest similarity with respect to the reference plots and QVI series with highest quality.</p>
34
Yadav, S. K., e 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 (26 luglio 2019): 535–39. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w6-535-2019.
Abstract (sommario):
<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&ndash;2017) temporal series Moderate Resolution Image Spectrometer (MODIS) data for Pre & 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 and ArcGIS image processing software are used to evaluate and monitor the vegetation for the pre-monsoon and post-monsoon seasons for three years. Enormous changes were observed during pre and post monsoon temporal analysis. This study shows that MODIS NDVI data is best suited for quick vegetation assessment in arid region.</p>
35
Bueno, Inacio T., Greg J. McDermid, Eduarda M. O. Silveira, Jennifer N. Hird, Breno I. Domingos e Fausto W. Acerbi Júnior. "Spatial Agreement among Vegetation Disturbance Maps in Tropical Domains Using Landsat Time Series". Remote Sensing 12, n. 18 (11 settembre 2020): 2948. http://dx.doi.org/10.3390/rs12182948.
Abstract (sommario):
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 disturbance maps provided by the Breaks For Additive Season and Trend (BFAST) algorithm, evaluating seven spectral indices in three distinct vegetation domains in Brazil: Atlantic forest, savanna, and semi-arid woodland, by assessing levels of agreement between the outputs. We computed individual map accuracies based on a reference dataset, then ranked their performance, while also observing their relationships with specific vegetation domains. Our results indicated a low rate of spatial agreement among index-based disturbance maps, which itself was minimally influenced by vegetation domain. Wetness indices produced greater detection accuracies in comparison to greenness-related indices free of saturation. The normalized difference moisture index performed best in the Atlantic forest domains, yet performed poorest in semi-arid woodland, reflecting its specific sensitivity to vegetation and its water content. The normalized difference vegetation index led to high disturbance detection accuracies in the savanna and semi-arid woodland domains. This study offered novel insight into vegetation disturbance maps, their relationship to different ecosystem types, and corresponding accuracies. Distinct input data can produce non-spatially correlated disturbance maps and reflect site-specific sensitivity. Future research should explore algorithm limitations presented in this study, as well as the expansion to other techniques and vegetation domains across the globe.
36
Morrison, K. A., e N. Thérien. "Release of Organic Carbon, Kjeldhal Nitrogen and Total Phosphorus from Flooded Vegetation". Water Quality Research Journal 31, n. 2 (1 maggio 1996): 305–18. http://dx.doi.org/10.2166/wqrj.1996.018.
Abstract (sommario):
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. In comparison with the ground vegetation species, the two tree species released more organic carbon (30–-60 mg C/g dry weight versus 3–6 mg) and total phosphorus (0.4–0.5 mg P/g dry weight versus 0.07–0.15 mg). However, all four substrates released the same order of magnitude of Kjeldahl nitrogen (0.1–0.6 mg N/g dry weight). Within each species, the two experimental series gave similar results for organic carbon and total phosphorus, but gave different results for Kjeldahl nitrogen, especially for the tree species. Exponential equations were fit to the data, and the estimated coefficients should be useful for estimating effects on water quality of submerged vegetation when reservoirs are flooded.
37
Økland, R. H., e E. Bendiksen. "The vegetation of the forest-alpine transition in the Grunningsdalen area, Telemark, S. Norway." Sommerfeltia 2, n. 1 (1 novembre 1985): 1–171. http://dx.doi.org/10.2478/som-1985-0002.
Abstract (sommario):
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 different regions is stressed. It is argued that four environmental gradients explain the major variation in Southern Norwegian forest and alpine vegetation. In the special part, the vegetation of the Grunningsdalen area is treated. Two gradients, the vertical gradient and the complex-gradient topographic moisture snow cover, are considered sufficient to explain the major variation in forest and alpine vegetation in the area. The vegetation is classified into 16 site-types by division of the gradients into four zones (according to altitude) and four series (according to moisture - snow cover) by means of floristic criteria known to reflect particular ecological conditions. For each of the site-types a description of the vegetation and an extensive comparison with corresponding Fennoscandian vegetation is given. On the basis of results from the present study area regional variation in Southern Norwegian poor vegetation corresponding to the xeric, subxeric, and submesic series, and phytosociological classification in the forestalpine transition are discussed. Various numerical classification and ordination methods are used in an analysis of the floristic composition of the site-types and the autecology of the species. The main phytosociological gradient in the investigated vegetation runs from dry and high altitude to wet and low altitude, most closely approaching the moisture gradient. Diversity relations are discussed. It is strongly emphasized that a hierarchic system is unable to give a consistent classification of a vegetation that must be regarded as a multidimensional network of variation along environmental gradients. Viewed in the light of the results of this study, a gradient approach to classification seems most suitable for a variety of Fennoscandian ecosystems.
38
Ba, Rui, Michele Lovallo, Weiguo Song, Hui Zhang e 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, n. 12 (29 novembre 2022): 1748. http://dx.doi.org/10.3390/e24121748.
Abstract (sommario):
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 modulating periodicities, with a larger spectral content at longer periods for two-fire-affected sites. Furthermore, two fires cause an increase of the persistence of the NDVI and EVI time series and an increase of the complexity, suggesting that the recovery process of vegetation dynamics of fire-affected sites is characterized by positive feedback mechanisms, driving the growth-generating phenomena, which become even more effective in those sites affected by two fires.
39
Jia, L., H. Shang, G. Hu e 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, n. 3 (25 marzo 2011): 1047–64. http://dx.doi.org/10.5194/hess-15-1047-2011.
Abstract (sommario):
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 catchment, river streamflow downstream of the modern irrigation schemes and satellite observations of vegetation index. Firstly, remotely sensed NDVI data acquired by Terra-MODIS are used to monitor the vegetation dynamic for a seven years period between 2000 and 2006. Due to cloud-contamination, atmospheric influence and different solar and viewing angles, however, the quality and consistence of time series of remotely sensed NDVI data are degraded. A Fourier Transform method – the Harmonic Analysis of Time Series (HANTS) algorithm – is used to reconstruct cloud- and noise-free NDVI time series data from the Terra-MODIS NDVI dataset. Modification is made on HANTS by adding additional parameters to deal with large data gaps in yearly time series in combination with a Temporal-Similarity-Statistics (TSS) method developed in this study to seek for initial values for the large gap periods. Secondly, the same Fourier Transform method is used to model time series of the vegetation phenology. The reconstructed cloud-free NDVI time series data are used to study the relationship between the water availability (i.e. the local precipitation and upstream water yield) and the evolution of vegetation conditions in Ejina Oasis from 2000 to 2006. Anomalies in precipitation, streamflow, and vegetation index are detected by comparing each year with the average year. The results showed that: the previous year total runoff had a significant relationship with the vegetation growth in Ejina Oasis and that anomalies in the spring monthly runoff of the Heihe River influenced the phenology of vegetation in the entire oasis. Warmer climate expressed by the degree-days showed positive influence on the vegetation phenology in particular during drier years. The time of maximum green-up is uniform throughout the oasis during wetter years, but showed a clear S-N gradient (downstream) during drier years.
40
Makarova, M. A. "Large-scale vegetation mapping of the Pinega river valley (the surroundings of Golubino village, Arkhangelsk oblast)". Geobotanical mapping, n. 2018 (2018): 19–39. http://dx.doi.org/10.31111/geobotmap/2018.19.
Abstract (sommario):
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 types of plant communities, 3 series, 14 combinations and 6 ecological series. The sites of old floodplain forests, such as willow forests with Urtica sondenii rare in the Arkhangelsk region and oxbow wet meadows with Scolochloa festucacea were identified.
41
Hua, Li, Huidong Wang, Haigang Sui, Brian Wardlow, Michael J. Hayes e Jianxun Wang. "Mapping the Spatial-Temporal Dynamics of Vegetation Response Lag to Drought in a Semi-Arid Region". Remote Sensing 11, n. 16 (10 agosto 2019): 1873. http://dx.doi.org/10.3390/rs11161873.
Abstract (sommario):
Drought, as an extreme climate event, affects the ecological environment for vegetation and agricultural production. Studies of the vegetative response to drought are paramount to providing scientific information for drought risk mitigation. In this paper, the spatial-temporal pattern of drought and the response lag of vegetation in Nebraska were analyzed from 2000 to 2015. Based on the long-term Daymet data set, the standard precipitation index (SPI) was computed to identify precipitation anomalies, and the Gaussian function was applied to obtain temperature anomalies. Vegetation anomaly was identified by dynamic time warping technique using a remote sensing Normalized Difference Vegetation Index (NDVI) time series. Finally, multilayer correlation analysis was applied to obtain the response lag of different vegetation types. The results show that Nebraska suffered severe drought events in 2002 and 2012. The response lag of vegetation to drought typically ranged from 30 to 45 days varying for different vegetation types and human activities (water use and management). Grasslands had the shortest response lag (~35 days), while forests had the longest lag period (~48 days). For specific crop types, the response lag of winter wheat varied among different regions of Nebraska (35–45 days), while soybeans, corn and alfalfa had similar response lag times of approximately 40 days.
42
Santana, Níckolas Castro, Osmar Abílio de Carvalho Júnior, Roberto Arnaldo Trancoso Gomes e 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, n. 11 (2 novembre 2020): 659. http://dx.doi.org/10.3390/ijgi9110659.
Abstract (sommario):
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 statistical significance. A comparison of time trends of EVI, GPP, and surface temperature with total fire recurrence indicates that time trends in vegetation are highly affected by high fire recurrence scenarios (R2 between 0.52 and 0.90). The fire recurrence and the albedo’s persistent changes do not have a consistent relationship. Areas with the biggest evaluated changes may increase up to 0.25 Kelvin/Year at surface temperature and decrease up to −0.012 EVI/year in vegetation index. Although savannas are resistant to low severity fires, fire regime and forest structure changes tend to make vegetation more vulnerable to wildfires, reducing their regeneration capacity. In the Amazon area, protection of forests in conservation units and indigenous lands helped in the low occurrence of fires in these sensitive areas, resulting in positive vegetation index trends.
43
van Leeuwen, Willem J. D., Grant M. Casady, Daniel G. Neary, Susana Bautista, José Antonio Alloza, Yohay Carmel, Lea Wittenberg, Dan Malkinson e Barron J. Orr. "Monitoring post-wildfire vegetation response with remotely sensed time-series data in Spain, USA and Israel". International Journal of Wildland Fire 19, n. 1 (2010): 75. http://dx.doi.org/10.1071/wf08078.
Abstract (sommario):
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 trends and metrics for burned areas are evaluated and compared with unburned reference sites to account for the influence of local environmental conditions. Time-series data interpretation provides insights into climatic influences on the post-fire vegetation. Although only two sites show increases in post-fire vegetation, all sites show declines in heterogeneity across the site. The evaluation of land surface phenological metrics, including the start and end of the season, the base and peak NDVI, and the integrated seasonal NDVI, show promising results, indicating trends in some measures of post-fire phenology. Results indicate that this monitoring approach, based on readily available satellite-based time-series vegetation data, provides a valuable tool for assessing post-fire vegetation response.
44
Sha, Zong Yao, e Yong Fei Bai. "Building Long-Term and Consistent Vegetation Index Based on Association Analysis between Different VI Products". Advanced Materials Research 518-523 (maggio 2012): 5261–66. http://dx.doi.org/10.4028/www.scientific.net/amr.518-523.5261.
Abstract (sommario):
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 both VI products provided consistent VI series. The consistency of the transformed dataset was further evaluated.
45
Zhou, Qu, Xianghan Sun, Liqiao Tian, Jian Li e Wenkai Li. "Grouping-Based Time-Series Model for Monitoring of Fall Peak Coloration Dates Using Satellite Remote Sensing Data". Remote Sensing 12, n. 2 (14 gennaio 2020): 274. http://dx.doi.org/10.3390/rs12020274.
Abstract (sommario):
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 challenging. In this study, a novel grouping-based time-series approach for satellite remote sensing was proposed, and a wide range of spectral wavelengths was considered to monitor the complex fall foliage coloration process with simultaneous changes in multiple vegetation properties. The spatial and temporal scale effects of satellite data were reduced to form a reliable remote sensing time-series, which was then divided into groups, namely pre-transition, transition and post-transition groups, to represent vegetation dynamics. The transition period of leaf coloration was correspondingly determined to divisions with the smallest intra-group and largest inter-group distances. Preliminary results using a time-series of Moderate Resolution Imaging Spectroradiometer (MODIS) data from 2002 to 2013 at the Harvard Forest (spatial scale: ~3500 m; temporal scale: ~8 days) demonstrated that the method can accurately determine the coloration period (correlation coefficient: 0.88; mean absolute difference: 3.38 days), and that the peak coloration periods displayed a shifting trend to earlier dates. The grouping-based approach shows considerable potential in phenological monitoring using satellite time-series.
46
Oliveira, Thomaz Chaves de Andrade, Luis Marcelo Tavares de Carvalho, Luciano Teixeira de Oliveira, Adriana Zanella Martinhago, Fausto Weimar Acerbi Júnior e Mariana Peres de Lima. "Mapping deciduous forests by using time series of filtered MODIS NDVI and neural networks". CERNE 16, n. 2 (giugno 2010): 123–30. http://dx.doi.org/10.1590/s0104-77602010000200002.
Abstract (sommario):
Multi-temporal images are now of standard use in remote sensing of vegetation during monitoring and classification. Temporal vegetation signatures (i. e., vegetation indices as functions of time) generated, poses many challenges, primarily due to signal to noise-related issues. This study investigates which methods generate the most appropriate smoothed curves of vegetation signatures on MODIS NDVI time series. The filtering techniques compared were the HANTS algorithm which is based on Fourier analyses and Wavelet temporal algorithm which uses the wavelet analysis to generate the smoothed curves. The study was conducted in four different regions of the Minas Gerais State. The smoothed data were used as input data vectors for vegetation classification by means of artificial neural networks for comparison purpose. A comparison of the results was ultimately discussed in this work showing encouraging results and similarity between the two filtering techniques used.
47
Bazan, Giuseppe, Pasquale Marino, Riccardo Guarino, Gianniantonio Domina e Rosario Schicchi. "Bioclimatology and Vegetation Series in Sicily: A Geostatistical Approach". Annales Botanici Fennici 52, n. 1-2 (aprile 2015): 1–18. http://dx.doi.org/10.5735/085.052.0202.
48
Meireles, Catarina, Carlos Pinto-Gomes e Eusebio Cano. "Approach to climatophilous vegetation series ofSerra da Estrela(Portugal)". Acta Botanica Gallica 159, n. 3 (settembre 2012): 283–87. http://dx.doi.org/10.1080/12538078.2012.737147.
49
Jamali, Sadegh, Per Jönsson, Lars Eklundh, Jonas Ardö e Jonathan Seaquist. "Detecting changes in vegetation trends using time series segmentation". Remote Sensing of Environment 156 (gennaio 2015): 182–95. http://dx.doi.org/10.1016/j.rse.2014.09.010.
50
Schwieder, Marcel, Pedro J. Leitão, Mercedes Maria da Cunha Bustamante, Laerte Guimarães Ferreira, Andreas Rabe e Patrick Hostert. "Mapping Brazilian savanna vegetation gradients with Landsat time series". International Journal of Applied Earth Observation and Geoinformation 52 (ottobre 2016): 361–70. http://dx.doi.org/10.1016/j.jag.2016.06.019.