scholarly journals Determining Spatial Parameters of the Ecological Niche of Parus Major (Passeriformes, Paridae) on the Base of Remote Sensing Data

2016 ◽  
Vol 50 (3) ◽  
pp. 251-258
Author(s):  
А. A. Zimaroeva ◽  
O. V. Zhukov ◽  
O. L. Ponomarenko
Keyword(s):  
Ecological Niche ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Parus Major ◽  
Remote Sensing Data ◽  
Great Tit ◽  
Ecological Niches ◽  
Water Index ◽  
Preference Map

Abstract Using factor analysis of ecological niches, we found that Parus major has high marginality to such ecogeographical variables (EGVs), as normalized difference vegetation index, the altitude above sea level, the diffuse insolation, activity of chlorophyll, normalized difference water index. This species is highly specialized in relation to various vegetation indices. Based on the type of habitat preference map, we found that Parus major doesn’t implement all its potential pro-spatial niche. Considering the ecological niche of great tit on different levels of scale, we noticed certain features: first, a list of factors that influence the distribution of great tit significantly altered by changing the scale, secondly, the factors that play a significant role in spreading Parus major on level of total consideration losing their weight and relevance on closer inspection (when the scale down); third, although specialization of great tits changes with the scale of consideration but Parus major mostly specialized by vegetation index.

scholarly journals Modeling of Durum Wheat Yield Based on Sentinel-2 Imagery

Agronomy ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1486
Author(s):  
Chris Cavalaris ◽  
Sofia Megoudi ◽  
Maria Maxouri ◽  
Konstantinos Anatolitis ◽  
Marios Sifakis ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Model Development ◽  
Wheat Yield ◽  
Growth Period ◽  
Yield Data ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Sentinel 2

In this study, a modelling approach for the estimation/prediction of wheat yield based on Sentinel-2 data is presented. Model development was accomplished through a two-step process: firstly, the capacity of Sentinel-2 vegetation indices (VIs) to follow plant ecophysiological parameters was established through measurements in a pilot field and secondly, the results of the first step were extended/evaluated in 31 fields, during two growing periods, to increase the applicability range and robustness of the models. Modelling results were examined against yield data collected by a combine harvester equipped with a yield-monitoring system. Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) were examined as plant signals and combined with Normalized Difference Water Index (NDWI) and/or Normalized Multiband Drought Index (NMDI) during the growth period or before sowing, as water and soil signals, respectively. The best performing model involved the EVI integral for the 20 April–31 May period as a plant signal and NMDI on 29 April and before sowing as water and soil signals, respectively (R2 = 0.629, RMSE = 538). However, model versions with a single date and maximum seasonal VIs values as a plant signal, performed almost equally well. Since the maximum seasonal VIs values occurred during the last ten days of April, these model versions are suitable for yield prediction.

scholarly journals Variation of MODIS reflectance and vegetation indices with viewing geometry and soybean development

2012 ◽  
Vol 84 (2) ◽  
pp. 263-274 ◽  
Author(s):  
Fábio M. Breunig ◽  
Lênio S. Galvão ◽  
Antônio R. Formaggio ◽  
José C.N. Epiphanio
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Constant Factor ◽  
The Other ◽  
Large Field ◽  
Phenological Stages ◽  
Water Index ◽  
Growing Seasons ◽  
Moderate Resolution Imaging Spectroradiometer

Directional effects introduce a variability in reflectance and vegetation index determination, especially when large field-of-view sensors are used (e.g., Moderate Resolution Imaging Spectroradiometer - MODIS). In this study, we evaluated directional effects on MODIS reflectance and four vegetation indices (Normalized Difference Vegetation Index - NDVI; Enhanced Vegetation Index - EVI; Normalized Difference Water Index - NDWI1640 and NDWI2120) with the soybean development in two growing seasons (2004-2005 and 2005-2006). To keep the reproductive stage for a given cultivar as a constant factor while varying viewing geometry, pairs of images obtained in close dates and opposite view angles were analyzed. By using a non-parametric statistics with bootstrapping and by normalizing these indices for angular differences among viewing directions, their sensitivities to directional effects were studied. Results showed that the variation in MODIS reflectance between consecutive phenological stages was generally smaller than that resultant from viewing geometry for closed canopies. The contrary was observed for incomplete canopies. The reflectance of the first seven MODIS bands was higher in the backscattering. Except for the EVI, the other vegetation indices had larger values in the forward scattering direction. Directional effects decreased with canopy closure. The NDVI was lesser affected by directional effects than the other indices, presenting the smallest differences between viewing directions for fixed phenological stages.

scholarly journals Exploring the Use of Vegetation Indices for Validating Crop Transpiration Fluxes Computed with the MOHID-Land Model. Application to Vineyard

Agronomy ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1228
Author(s):  
Tiago B. Ramos ◽  
Lucian Simionesei ◽  
Ana R. Oliveira ◽  
Ramiro Neves ◽  
Hanaa Darouich
Keyword(s):  
Remote Sensing ◽  
Water Balance ◽  
Soil Water ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Remote Sensing Data ◽  
Soil Water Balance ◽  
Growing Seasons ◽  
Crop Coefficients

The success of an irrigation decision support system (DSS) much depends on the reliability of the information provided to farmers. Remote sensing data can expectably help validate that information at the field scale. In this study, the MOHID-Land model, the core engine of the IrrigaSys DSS, was used to simulate the soil water balance in an irrigated vineyard located in southern Portugal during three growing seasons. Modeled actual basal crop coefficients and transpiration rates were then compared with the corresponding estimates derived from the normalized difference vegetation index (NDVI) computed from Sentinel-2 imagery. On one hand, the hydrological model was able to successfully estimate the soil water balance during the monitored seasons, exposing the need for improved irrigation schedules to minimize percolation losses. On the other hand, remote sensing products found correspondence with model outputs despite the conceptual differences between both approaches. With the necessary precautions, those products can be used to complement the information provided to farmers for irrigation of vine crop, further contributing to the regular validation of model estimates in the absence of field datasets.

scholarly journals DETECTION AGRICULTURE DEGRADATION FOR THE SOUTH OF BAGHDAD CITY USING REMOTE SENSING DATA FOR YEARS 2010-2019

2020 ◽  
pp. 57-67
Author(s):  
Taif Adil DHAMIN ◽  
Ebtesam F. KHANJER ◽  
Fouad K. MASHEE
Keyword(s):  
Remote Sensing ◽  
Land Degradation ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Remote Sensing Data ◽  
Military Operations ◽  
The South ◽  
Water Index ◽  
Rs And Gis ◽  
Landsat Satellite

Recently, the develop of the science of remote sensing enabled humanity to achieve the accuracy and wide coverage for different natural phenomena, disasters and applications (such as desertification, rainstorms, floods, fires, sweeping torrents, urban planning, and even in military). The main aim of this study is monitoring, highlighting and assessing maps for the degradation of agriculture in the south areas of Baghdad governorate (Al-Rasheed, Al-Yusufiyah, Al-Mahmudiyah, Al-Latifiyah, and Al-Madaen). Based to several factors, including the economic, social and military operations, the area had suffer the lands degradation which led to agriculture retreating. Remote sensing and Geographic information system (GIS) was applied, using ArcGIS 10.4.1 to process, manage, and analysis datasets, beside field verification to estimate the severity assessment of a computerized land degradation. Two satellites were adapted Landsat5 TM+ and Landsat8 OLI/TIRS imageries to assess the extent of land degradation for the study area during the years (5th May 2010 and 2nd May 2019). Two indices used in this research are: The Normalized Difference Vegetation Index “NDVI”, and The Normalized Differential Water Index “NDWI”. The results showed that there is a clear spatial reduction in both NDVI and NDWI, where the NDVI reduced from 2461082400 m2 to 1552698000 m2, accounting for 89.67 and 56.57 percent, respectively, while the NDWI reduced from 14166000 m2 to 12053700 m2, accounting for 0.52, and 0.44 percent, respectively. Keywords: Agriculture Degradation, RS And GIS Techniques, Landsat Satellite Imagery, NDVI And NDWI.

scholarly journals Assessment of the Hail Damage on Agricultural Crops from North of Baragan Plain, Romania, Using Remote Sensing Data

2021 ◽  
Author(s):  
Claudiu Valeriu Angearu ◽  
Irina Ontel ◽  
Anisoara Irimescu ◽  
Burcea Sorin
Keyword(s):  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Remote Sensing Data ◽  
Weather Radar ◽  
Radar Data ◽  
Landsat 8 ◽  
Water Index ◽  
Hail Damage ◽  
Sentinel 2A

Abstract Hail is one of the dangerous meteorological phenomena facing society. The present study aims to analyze the hail event from 20 July 2020, which affected the villages of Urleasca, Traian, Silistraru and Căldăruşa from the Traian commune, Baragan Plain. The analysis was performed on agricultural lands, using satellite images in the optical domain: Sentinel-2A, Landsat-8, Terra MODIS, as well as the satellite product in the radar domain: Soil Water Index (SWI), and weather radar data. Based on Sentinel-2A images, a threshold of 0.05 of the Normalized Difference Vegetation Index (NDVI) difference was established between the two moments of time analyzed (14 and 21 July), thus it was found that about 4000 ha were affected. The results show that the intensity of the hail damage was directly proportional to the Land Surface Temperature (LST) difference values in Landsat-8, from 15 and 31 July. Thus, the LST difference values higher than 12° C were in the areas where NDVI suffered a decrease of 0.4-0.5. The overlap of the hail mask extracted from NDVI with the SWI difference situation at a depth of 2 cm from 14 and 21 July confirms that the phenomenon recorded especially in the west of the analyzed area, highlighted by the large values (greater than 55 dBZ) of weather radar reflectivity as well, indicating medium–large hail size. This research also reveals that satellite data is useful for cross validation of surface-based weather reports and weather radar derived products.

scholarly journals IMPACT ASSESSMENT OF A MINE SUBSIDENCE ON NATIVE VEGETATION OF SOUTH EASTERN COALFIELDS, INDIA

2018 ◽  
Vol XLII-5 ◽  
pp. 487-490
Author(s):  
A. K. Vishwakarma ◽  
A. K. Agnihotri ◽  
R. Rai ◽  
B. K. Shrivastva ◽  
S. Mishra
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Remote Sensing Data ◽  
Adverse Impact ◽  
Native Vegetation ◽  
Landsat 8 ◽  
Vegetation Growth ◽  
South Eastern ◽  
Wide Range

<p><strong>Abstract.</strong> This study aims to evaluate the effect of underground coal mining subsidence on the growth of native vegetation. For this study, an underground coal mine of South Eastern Coalfields Limited (SECL), India was selected. Changes in vegetation indices were analyzed using three remote sensing data of the previous five years. Three period’s Landsat 8 OLI resolution image data were used to calculate Normalized Difference Vegetation Index (NDVI) of the years 2014, 2016 and 2018 in QGIS environment. The study showed that the local grassland and forest were affected by the mining exploitation and subsidence but those effects were not significant to have an adverse impact on the same. The short-term mining was having an impact on the vegetation growth but the effects gradually disappeared with the gradual stabilization of the subsided land and in absence of human interference, vegetation recovered well. In long-term, subsidence was not having a major impact on the vegetation growth. Thus, coal resources exploitation and subsidence of the said mine of SECL did not bring out an adverse impact on a wide range of forest and grassland ecosystems, and these ecosystems could carry the partial destruction and ultimately stabilized ecosystems by self-repair.</p>

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 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.

scholarly journals COMPARISON OF VEGETATION INDICES FROM RPAS AND SENTINEL-2 IMAGERY FOR DETECTING PERMANENT PASTURES

2018 ◽  
Vol XLII-3 ◽  
pp. 1381-1387 ◽  
Author(s):  
M. Piragnolo ◽  
G. Lusiani ◽  
F. Pirotti
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Agricultural Land ◽  
Vegetation Indices ◽  
Monitoring And Control ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Remote Sensing Techniques ◽  
Sentinel 2

Permanent pastures (PP) are defined as grasslands, which are not subjected to any tillage, but only to natural growth. They are important for local economies in the production of fodder and pastures (Ali et al. 2016). Under these definitions, a pasture is permanent when it is not under any crop-rotation, and its production is related to only irrigation, fertilization and mowing. Subsidy payments to landowners require monitoring activities to determine which sites can be considered PP. These activities are mainly done with visual field surveys by experienced personnel or lately also using remote sensing techniques. The regional agency for SPS subsidies, the Agenzia Veneta per i Pagamenti in Agricoltura (AVEPA) takes care of monitoring and control on behalf of the Veneto Region using remote sensing techniques. The investigation integrate temporal series of Sentinel-2 imagery with RPAS. Indeed, the testing area is specific region were the agricultural land is intensively cultivated for production of hay harvesting four times every year between May and October. The study goal of this study is to monitor vegetation presence and amount using the Normalized Difference Vegetation Index (NDVI), the Soil-adjusted Vegetation Index (SAVI), the Normalized Difference Water Index (NDWI), and the Normalized Difference Built Index (NDBI). The overall objective is to define for each index a set of thresholds to define if a pasture can be classified as PP or not and recognize the mowing.

scholarly journals Assessment of soil contamination using remote sensing data in the Tamsag-Bulag oil field, Mongolia

2021 ◽  
Vol 895 (1) ◽  
pp. 012013
Author(s):  
S Gantumur ◽  
G V Kharitonova ◽  
A S Stepanov ◽  
K N Dubrovin
Keyword(s):  
Remote Sensing ◽  
Satellite Images ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Soil Cover ◽  
Vegetation Indices ◽  
Remote Sensing Data ◽  
Oil Field ◽  
Oil Contamination ◽  
Vegetation Growth

Abstract Although field surveus represent an essential method for determining oil contamination of soils and soil cover, the use of remote sensing techniques has become one of the main trends over recent years due to their economic and temporary advantages. The fundamental basis of this approach is the assessment of changes in vegetation cover by vegetation indices as indicator. In this study, the problems of assessment of the soil cover contamination during oil production are considered. It is aimed to select and evaluate objective criteria for soil cover contamination with oil in the Tamsag–Bulag field (Eastern Gobi, Mongolia). For this purpose, during the period of maximum vegetation growth, various vegetation indices were investigated at test sites (4 km2) from 2015 to 2019. The Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) were used with Sentinel-2 and MODIS of the Terra satellite images at 30 and 250 m resolution, respectively. The monitoring of the land quality with satellite images via NDVI and SAVI allows us to assess the area of oil contamination of the soils and soil cover. The significant increase in the values of the NDVI and SAVI at a distance of more than 4 km from the center of the Tamsag-Bulag oil field is shown. The obtained results indicate the possibility of assessment and monitoring the state of the oil-ed territories of the Eastern Gobi by NDVI и SAVI using satellite images.

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