Seasonal and Long-Term Features of the Distribution of the Vegetation Index NDVI on Arable Lands in Bryansk Region

2018 ◽  
Vol 9 (1) ◽  
pp. 34
Author(s):  
Gregory V. LOBANOV ◽  
Boris V. TRISHKIN ◽  
Marina V. AVRAMENKO ◽  
Anna Yu. CHAROCHKINA ◽  
Alina P. PROTASOVA
Keyword(s):  
Seasonal Dynamics ◽  
Vegetation Index ◽  
Humus Content ◽  
Arable Land ◽  
Vegetation Indices ◽  
Vegetation Period ◽  
Surface Slope ◽  
Mean Values ◽  
Complex Ratio

The features of the multi-year distribution of vegetation indices are considered as an indicator of the favorable conditions for farming, the correspondence of the set of crops with the soil, geomorphological, microclimatic characteristics of the agrolandscape. The authors presented methodical problems of using information about NDVI for remote determination and monitoring of soil properties through bioproductivity. Methods of collecting and systematizing information on the seasonal and long-term dynamics of NDVI (according to MODIS data) and some factors of the spatial distribution of its mean values were used for a detailed study of the issue. The possible mechanisms of influence of the surface slope (the values are determined by SRTM), the average humus content, the mechanical strength of soils on the NDVI distribution were shown. Information on NDVI arable land changes in the Bryansk region in the years with different meteorological conditions (2010-2015) was given. The general features of NDVI seasonal dynamics within the vegetation period were described – from late March to early November. The influence of meteorological factors on the distribution of NDVI was determined. Synchronous differences in the dynamics of NDVI for groups of arable land plots homogeneous in the state of the surface slope and agrotechnical characteristics of soils were justified from the position of seasonal dynamics of landscapes. A complex ratio of the significance of groups of factors was shown, possible models for explaining their differences in space and time were proposed.

scholarly journals SEASONAL AND LONG-TERM CHANGES OF VEGETATION INDEX OF ARABLE LANDS OF BRYANSK REGION (CENTRAL RUSSIA): REGULARITIES AND DYNAMICS FACTORS

2020 ◽  
Vol 17 (34) ◽  
pp. 745-755
Author(s):  
Grigory V LOBANOV ◽  
Marina V AVRAMENKO ◽  
Alina P PROTASOVA ◽  
Nikolai N DROZDOV
Keyword(s):  
Vegetation Index ◽  
Humus Content ◽  
Arable Land ◽  
Vegetation Indices ◽  
Climatic Fluctuations ◽  
The Sustainable Development ◽  
Central Russia ◽  
Long Term Changes ◽  
Agricultural Use

There are a number of different factors that affect the sustainable development of agriculture. Efficient use of land and water resources, taking into account the threats that may arise due to climate change, as well as the dynamics of plant growth and development. Vegetation indices play an important role in monitoring vegetation variations. This article provides information on seasonal and long-term changes in the EVI (Enhanced Vegetation Index) of arable land in the Bryansk region. The purpose of the article is to discern the course of the seasonal dynamics of the index, the ranges of its variability, probable causes of differences between 2000-2018. In this article, physicochemical research methods were used to calculate the humus content, surface topography characteristics, and agricultural use features based on cartographic and stock materials, and the soil composition and density were determined through fieldwork. Summarized material on changes in EVI values for a snowless period as a whole and for individual filming intervals (18 per year) in 2000-2018 is presented. The mechanism of the influence of a variety of edaphic vegetation conditions on smoothing the differences in EVI over a series of years with different meteorological conditions has been described. The results of the analysis of the factors of the long-term dynamics are presented, the role of short-term climatic fluctuations, a progressive decrease in the amount of precipitation, and changes in the species composition of grain crops in the long-term dynamics of EVI are demonstrated.

scholarly journals The Ongoing Greening in Southwest China despite Severe Droughts and Drying Trends

2021 ◽  
Vol 13 (17) ◽  
pp. 3374
Author(s):  
Xin Chen ◽  
Tiexi Chen ◽  
Qingyun Yan ◽  
Jiangtao Cai ◽  
Renjie Guo ◽  
...  
Keyword(s):  
Vegetation Change ◽  
Vegetation Index ◽  
Southwest China ◽  
Vegetation Indices ◽  
Climate Extremes ◽  
Global Scale ◽  
Area Index ◽  
Huge Impact ◽  
Increasing Trends

Vegetation greening, which refers to the interannual increasing trends of vegetation greenness, has been widely found on the regional to global scale. Meanwhile, climate extremes, especially several drought, significantly damage vegetation. The Southwest China (SWC) region experienced massive drought from 2009 to 2012, which severely damaged vegetation and had a huge impact on agricultural systems and life. However, whether these extremes have significantly influenced long-term (multiple decades) vegetation change is unclear. Using the latest remote sensing-based records, including leaf area index (LAI) and gross primary productivity (GPP) for 1982–2016 and enhanced vegetation index (EVI) for 2001–2019, drought events of 2009–2012 only leveled off the greening (increasing in vegetation indices and GPP) temporally and long-term greening was maintained. Meanwhile, drying trends were found to unexpectedly coexist with greening.

scholarly journals Vegetation indices in the prediction of biomass and grain yield of white oat under irrigation levels

2018 ◽  
Vol 48 (2) ◽  
pp. 109-117 ◽  
Author(s):  
Anderson Prates Coelho ◽  
David Luciano Rosalen ◽  
Rogério Teixeira de Faria
Keyword(s):  
Grain Yield ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Flag Leaf ◽  
Vegetation Indices ◽  
Leaf Sheath ◽  
Phenological Stage ◽  
Mean Values ◽  
Ripe Stage ◽  
Irrigation Levels

ABSTRACT Vegetation indices are widely used to indicate the nutritional status of crops, as well as to estimate their harvest yield. However, their accuracy is influenced by the phenological stage of evaluation and the index used. The present study aimed to evaluate the accuracy of the Normalized Difference Vegetation Index (NDVI) and Inverse Ratio Vegetation Index (IRVI) in the prediction of grain yield and biomass of white oat cultivated under irrigation levels, besides indicating the best phenological stage for evaluation. The irrigation levels consisted of 11 %, 31 %, 60 %, 87 % and 100 % of the maximum evapotranspiration, with four replicates. The mean values for NDVI and IRVI were determined using an active terrestrial sensor, at four phenological stages (4, 8, 10 and 10.5.4). The white oat grain yield and biomass may be estimated with a high precision using the NDVI and IRVI. The NDVI was more accurate than the IRVI. The grain yield estimate was more accurate from the flag leaf sheath appearance stage (10), whereas, for the biomass, the best estimate was for the kernel watery ripe stage (10.5.4).

scholarly journals Spectral Index-Based Monitoring (2000–2017) in Lowland Forests to Evaluate the Effects of Climate Change

Geosciences ◽  
2019 ◽  
Vol 9 (10) ◽  
pp. 411 ◽  
Author(s):  
Ferenc Kovács ◽  
András Gulácsi
Keyword(s):  
Climate Change ◽  
Biomass Production ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Climate Extremes ◽  
Vegetation Period ◽  
Great Plain ◽  
Data Series ◽  
Water Shortages

In the next decades, climate change will put forests in the Hungarian Great Plain in the Carpathian Basin to the test, e.g., changing seasonal patterns, more intense storms, longer dry periods, and pests are expected to occur. To aid in the decision-making process for the conservation of ecosystems depending on forestry, how woods could adapt to changing meso- and microclimatic conditions in the near future needs to be defined. In addition to trendlike warming processes, calculations show an increase in climate extremes, which need to be monitored in accordance with spatial planning, at least for medium-scale mappings. We can use the MODIS sensor dataset if up-to-date terrestrial conditions and multi-decadal geographical processes are of interest. For geographic evaluations of changes, we used vegetation spectral indices; Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI), based on the summer half year, 16-day MODIS data composites between 2000 and 2017 in an intensively forested study area in the Hungarian Great Plain. We delineated forest areas on the Danube–Tisza Interfluve using Corine Land Cover maps (2000, 2006, and 2012). Mid-year changes over the nearly two-decade-long period are currently in balance; however, based on their reactions, forests are highly sensitive to abrupt changes caused by extreme climatic events. The higher occurrence of years or periods with extreme water shortages marks an observable decrease in biomass production, even in shorter index time series, such as that between 2004 and 2012. In the drought-stricken July-August periods, the effect of a dry year, subsequent to years with more precipitation, immediately pushes back the green mass and the reduction in the biomass production could become persistent, according to climatology predictions. The changes of specific sub-periods in the vegetation period can be evaluated even in a relatively short, 18-year data series, including the change in the growing values of the vegetative growth in spring or the increase in the summertime biomass production. Standardized differences highlight spatial differences in the biomass production; in response to years with the highest (negative) biomass difference; typically, the northern and southwestern parts of the Danube–Tisza Interfluve in the study area have longer lasting losses in biomass production. A comparison of NDVI and EVI values with the PaDI drought index and the vegetation indices of LANDSAT Operational Land Imager sensor respectively confirms our results.

Die Vegetationsentwicklung – erfasst am Individuum und über den Raum | Changes in plant development – monitored on the individual plant and over geographical area

2007 ◽  
Vol 158 (7) ◽  
pp. 221-228 ◽  
Author(s):  
Robert Brügger ◽  
Sibylle Studer ◽  
Reto Stöckli
Keyword(s):  
Vegetation Index ◽  
Vegetation Indices ◽  
Geographical Area ◽  
Monitoring Network ◽  
Vegetation Period ◽  
Individual Plant ◽  
Summer Drought ◽  
Pollution Effects ◽  
Icp Forests ◽  
The Individual

The phenological development of plants provides information about the influence of weather on vegetation and may be assessed on both the individual plant level and on a global level. Since 2000, Switzerland has had a phenological monitoring network for forest trees which records the seasonality and is complementary to the ICP-Forests Assessments (International Co-operative Programme on Assessment and Monitoring of Air Pollution Effects on Forests). A comparison of leaf discoloration at the monitoring plot Kaiseraugst has shown that beech, oak and ash trees have all been affected differently as a result of the summer drought of 2003. In 2004,a digital picture data base was developed for the research project 'Phenophot' at the Geographical Institute Berne which allows the phenological observations to become objective and reproducible. A Phenological Growing Index (PGI) is being derived from the red-green-blue channel data of the digital sensor, which complements the information of satellite based vegetation indices. These indices include the Normalized Differenced Vegetation Index (NDVI) on a sub-pixel level which provides improved accuracy for the information on the character and beginning and ending of the vegetation period. The first comparison of the phenological spring index from the satellite based NDVI revealed that the annual start of spring is reproduced most accurately by determining a threshold of the NDVI.

Effects of Seasonal and Perennial Climatic Fluctuations on Geographical Distribution of the Enhanced Vegetation Index EVI of Arable Land in the Bryansk Region

2019 ◽  
Vol 10 (3) ◽  
pp. 669
Author(s):  
Grigory V. LOBANOV ◽  
Marina V. AVRAMENKO ◽  
Anna Yu. CHAROCHKINA ◽  
Nikolay N. DROZDOV
Keyword(s):  
Geographical Distribution ◽  
Vegetation Index ◽  
Agricultural Land ◽  
Humus Content ◽  
Arable Land ◽  
Distribution Patterns ◽  
Climatic Conditions ◽  
Climatic Fluctuations ◽  
Enhanced Vegetation Index ◽  
Lithological Composition

This article discusses the patterns of geographical distribution of the enhanced vegetation index EVI within the Bryansk region (upper Dnieper basin, south-western Russia) in the spring months of 2010-2015. The factors of index distribution, based on agricultural land monitoring data in other regions, are described. The crucial role of abiotic (topography, soil) and biotic factors in the distribution of the EVI is shown. The generalized data of meteorological observations of 2010-2015 are presented; the effects of their high variability on the range of the EVI values and its geographical distribution are shown. Data on the differences in the EVI distribution in the spring months of 2010-2015 is presented, which are explained by the differing periods of phenological seasons, surface relief characteristics (flat and convex watersheds, drainage conditions), lithological composition and humus content in the upper horizons of arable soil. A qualitative relationship between spring changes in the EVI for arable land and the combination of edaphic factors of agro-landscape functioning is established and the mechanisms that ensure such differences are presented. The use of the EVI distribution patterns is justified in the years with different climatic conditions to identify arable lands with different surface topography and soil characteristics.

scholarly journals ON THE INFORMATION CONTENT OF SPECTRAL CHANNELS AND NDVI OF THE LANDSAT 5 TM LANDSAT 7 ETM+ REMOTE SENSING SATELLITE FOR ASSESSING THE STAGES OF FOREST OVERGROWTH OF ABANDONED AGRICULTURAL LANDS

ÈKOBIOTEH ◽  
2021 ◽  
Vol 4 (3) ◽  
pp. 178-185
Author(s):  
I.R. Tuktamyshev ◽  
◽  
P.S. Shirokikh ◽  
R.Y. Mullagulov ◽  
◽  
...  
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Arable Land ◽  
Vegetation Indices ◽  
Remote Sensing Data ◽  
The Urals ◽  
Landsat 7 ◽  
Landsat 5 Tm ◽  
The Difference

Abandoned arable land is a widespread phenomenon in land use. Methods based on the use of remote sensing data are most suitable for studying and monitoring farmlands overgrown with forest. Multispectral satellite images and vegetation indices can reflect the difference at certain stages of the successional development of fallow vegetation. The aim of the work is to evaluate the informative value of individual channels of medium-resolution images of Landsat satellites and the normalized difference vegetation index (NDVI) for identifying vegetation areas at various stages of reforestation succession on abandoned arable land in the zone of distribution of broad-leaved forests in the Urals. As the source material we used 30 georeferenced relevés of different overgrowth stages made in 2012, and 9 cloudless Landsat 5 TM and Landsat 7 ETM+ images for the period from April to October 2011. Using the data, NDVI and values of three spectral bands (Red, NIR, Thermal) were calculated for the relevé points. The most informative when dividing the stages of reforestation on abandoned fields in the zone of distribution of broad-leaved forests in the Urals were the NDVI vegetation index and the surface temperature estimated by the thermal channel. In addition, the red band can be useful for identifying the initial stage of succession.

scholarly journals Leaf Area Index Variations in Ecoregions of Ardabil Province, Iran

2021 ◽  
Vol 13 (15) ◽  
pp. 2879
Author(s):  
Lida Andalibi ◽  
Ardavan Ghorbani ◽  
Mehdi Moameri ◽  
Zeinab Hazbavi ◽  
Arne Nothdurft ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Google Earth ◽  
Spatial And Temporal Variability ◽  
Landsat 8 ◽  
Area Index ◽  
Mean Values

The leaf area index (LAI) is an important vegetation biophysical index that provides broad information on the dynamic behavior of an ecosystem’s productivity and related climate, topography, and edaphic impacts. The spatiotemporal changes of LAI were assessed throughout Ardabil Province—a host of relevant plant communities within the critical ecoregion of a semi-arid climate. In a comparative study, novel data from Google Earth Engine (GEE) was tested against traditional ENVI measures to provide LAI estimations. Moreover, it is of important practical significance for institutional networks to quantitatively and accurately estimate LAI, at large areas in a short time, and using appropriate baseline vegetation indices. Therefore, LAI was characterized for ecoregions of Ardabil Province using remote sensing indices extracted from Landsat 8 Operational Land Imager (OLI), including the Enhanced Vegetation Index calculated in GEE (EVIG) and ENVI5.3 software (EVIE), as well as the Normalized Difference Vegetation Index estimated in ENVI5.3 software (NDVIE). Moreover, a new field measurement method, i.e., the LaiPen LP 100 portable device (LP 100), was used to evaluate the accuracy of the derived indices. Accordingly, the LAI was measured in June and July 2020, in 822 ground points distributed in 16 different ecoregions-sub ecoregions having various plant functional types (PFTs) of the shrub, bush, and tree. The analyses revealed heterogeneous spatial and temporal variability in vegetation indices and LAIs within and between ecoregions. The mean (standard deviation) value of EVIG, EVIE, and NDVIE at a province scale yielded 1.1 (0.41), 2.20 (0.78), and 3.00 (1.01), respectively in June, and 0.67 (0.37), 0.80 (0.63), and 1.88 (1.23), respectively, in July. The highest mean values of EVIG-LAI, EVIE-LAI, and NDVIE-LAI in June are found in Meshginshahr (1.40), Meshginshahr (2.80), and Hir (4.33) ecoregions and in July are found in Andabil ecoregion respectively with values of 1.23, 1.5, and 3.64. The lowest mean values of EVIG-LAI, EVIE-LAI, and NDVIE-LAI in June were observed for Kowsar (0.67), Meshginshahr (1.8), and Neur (2.70) ecoregions, and in July, the Bilesavar ecoregion, respectively, with values of 0.31, 0.31, and 0.81. High correlation and determination coefficients (r > 0.83 and R2 > 0.68) between LP 100 and remote sensing derived LAI were observed in all three PFTs (except for NDVIE-LAI in June with r = 0.56 and R2 = 0.31). On average, all three examined LAI measures tended to underestimate compared to LP 100-LAI (r > 0.42). The findings of the present study could be promising for effective monitoring and proper management of vegetation and land use in the Ardabil Province and other similar areas.

scholarly journals THE ESTIMATE OF THE SPATIAL-TEMPORAL FEATURES OF VEGETATION COVER OF KAZAKHSTAN BASED ON TIME SERIES SATELLITE INDECES IN 2000-2015

2016 ◽  
Vol XLI-B8 ◽  
pp. 1067-1071
Author(s):  
I. Vitkovskaya ◽  
M. Batyrbayeva ◽  
L. Spivak
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Weather Conditions ◽  
Condition Index ◽  
Negative Trend ◽  
Positive Trend ◽  
High Productivity ◽  
Temporal Features

The article presents the evaluation of spatial-temporal characteristics of Kazakhstan arid and semi-arid areas' vegetation on the basis of time series of differential and integral vegetation indices. It is observed the negative trend of integral indices for the period of 2000-2015. This fact characterizes the increase of stress influence of weather conditions on vegetation in Kazakhstan territory during last decade. Simultaneously there is a positive trend of areas of zones with low values of IVCI index. Zoning of the territory of Kazakhstan was carried out according to the long-term values of the normalized integral vegetation index, which is characteristic of the accumulated amount of green season biomass. Negative trend is marked for areas of high productivity zones, long-term changes in the areas of low productivity zones have tend to increase. However long-term values of the area of the middle zone are insignificantly changed. Location boundaries of this zone in the latitudinal direction connects with a weather conditions of the year: all wet years, the average area is located between 46°- 49°N, and the all dry years - between 47°30'- 54°N. The map of frequency of droughts was formed by low values of the integral vegetation condition index which calculated from satellite data.

scholarly journals Development of forest cover mask to monitor the health condition of forests in Poland using long-term satellite observations

2016 ◽  
Vol 77 (2) ◽  
pp. 141-150
Author(s):  
Maciej Bartold
Keyword(s):  
Vegetation Index ◽  
Forest Cover ◽  
Normalized Difference Vegetation Index ◽  
Meteorological Data ◽  
Vegetation Indices ◽  
Forest Health ◽  
Remote Sensing Data ◽  
Forest Regions ◽  
The Impact

Abstract The work presented here aims at developing cover mask for monitoring forest health in Poland using remote sensing data. The main objective was to assess the impact of using the mask on forest condition monitoring combined with vegetation indices obtained from long-term satellite data. In this study, a new mask developed from the CORINE Land Cover 2012 (CLC2012) database is presented and its one-kilometer pixel size matched to low-resolution data derived from SPOT VEGETATION satellite registrations. For vegetation mapping, only pixels with a cover ≥ 50% of broad-leaved and mixed forests defined by CLC2012 were taken into account. The masked pixels were used to evaluate spatial variability in eight Natural-Forest Regions (NFRs). The largest coverages by masked forests were obtained in Sudetian (65.7%), Carpathian (65.9%) and Baltic (51.3%) regions. For other forest regions the coverage was observed to be around 30-50%. Time-series of the Normalized Difference Vegetation Index (NDVI) comprising SPOT VEGETATION images from 1998 until 2014 were computed and cross-comparison analyses on ≥ 50% and < 50% forest cover masks brought up frequent differences at a level higher than 0.05 NDVI in seven out of eight NFRs. An exception is the Sudetian region, where the data was highly consistent. Furthermore, the Mann-Whitney U non-parametric test revealed statistically significant differences in two regions: Baltic and Masurian-Podlasie NFR. The comparative analysis of NDVI confirmed that there is a need for additional investigation of the quality of newly developed forest mask combined with vegetation and meteorological data.

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