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 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.

scholarly journals Estimating Crop Yields at the Field Level Using Landsat and Modis Products

2018 ◽  
Vol 66 (5) ◽  
pp. 1141-1150 ◽  
Author(s):  
František Jurečka ◽  
Vojtěch Lukas ◽  
Petr Hlavinka ◽  
Daniela Semerádová ◽  
Zdeněk Žalud ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Crop Yields ◽  
Spring Barley ◽  
Vegetation Indices ◽  
Remote Sensing Data ◽  
Landsat 8 ◽  
Yield Data ◽  
Field Level

Remote sensing can be used for yield estimation prior to harvest at the field level to provide helpful information for agricultural decision making. This study was undertaken in Polkovice, located at low elevations in the Czech Republic. From 2014–2016, two datasets of satellite imagery were used: the Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat 8 datasets. Satellite data were compared with yields and other observations at the level of land blocks. Winter oilseed rape, winter wheat and spring barley yield data, representing the crops planted over the analyzed period, were used for comparison. In 2016, a more detailed analysis was conducted. We tested a relationship between remote sensing data and the spatial yield variability measured by a yield monitor from a combine harvester. Correlations varied from approximately r = 0.4 to r = 0.7 with the highest correlation (r = 0.74) between yield and the Green Normalized Difference Vegetation Index collected from a drone. Vegetation indices from both Landsat 8 and the MODIS showed a positive relationship with yields for the compared period. The highest correlation was between yield and the Enhanced Vegetation Index (r = 0.8) while the lowest was between yield and the Normalized Difference Vegetation Index from MODIS (r = 0.1).

scholarly journals Consideration of NDVI thematic changes in density analysis and floristic composition of Wadi Yalamlam, Saudi Arabia

2018 ◽  
Vol 7 (4) ◽  
pp. 297-306 ◽  
Author(s):  
Amal Y. Aldhebiani ◽  
Mohamed Elhag ◽  
Ahmad K. Hegazy ◽  
Hanaa K. Galal ◽  
Norah S. Mufareh
Keyword(s):  
Saudi Arabia ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Remote Sensing Data ◽  
Floristic Composition ◽  
Water Source ◽  
Shannon Index ◽  
Landsat 8 ◽  
Main Stream

Abstract. Wadi Yalamlam is known as one of the significant wadis in the west of Saudi Arabia. It is a very important water source for the western region of the country. Thus, it supplies the holy places in Mecca and the surrounding areas with drinking water. The floristic composition of Wadi Yalamlam has not been comprehensively studied. For that reason, this work aimed to assess the wadi vegetation cover, life-form presence, chorotype, diversity, and community structure using temporal remote sensing data. Temporal datasets spanning 4 years were acquired from the Landsat 8 sensor in 2013 as an early acquisition and in 2017 as a late acquisition to estimate normalized difference vegetation index (NDVI) changes. The wadi was divided into seven stands. Stands 7, 1, and 3 were the richest with the highest Shannon index values of 2.98, 2.69, and 2.64, respectively. On the other hand, stand 6 has the least plant biodiversity with a Shannon index of 1.8. The study also revealed the presence of 48 different plant species belonging to 24 families. Fabaceae (17 %) and Poaceae (13 %) were the main families that form most of the vegetation in the study area, while many families were represented by only 2 % of the vegetation of the wadi. NDVI analysis showed that the wadi suffers from various types of degradation of the vegetation cover along with the wadi main stream.

scholarly journals An object-based approach for semi-automated landslide change detection and attribution of changes to landslide classes in northern Taiwan

2015 ◽  
Vol 8 (2) ◽  
pp. 327-335 ◽  
Author(s):  
Daniel Hölbling ◽  
Barbara Friedl ◽  
Clemens Eisank
Keyword(s):  
Change Detection ◽  
Spatial Resolution ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Remote Sensing Data ◽  
Detection Methods ◽  
Detection And Attribution ◽  
Object Based ◽  
Wide Range ◽  
Northern Taiwan

Abstract Earth observation (EO) data are very useful for the detection of landslides after triggering events, especially if they occur in remote and hardly accessible terrain. To fully exploit the potential of the wide range of existing remote sensing data, innovative and reliable landslide (change) detection methods are needed. Recently, object-based image analysis (OBIA) has been employed for EO-based landslide (change) mapping. The proposed object-based approach has been tested for a sub-area of the Baichi catchment in northern Taiwan. The focus is on the mapping of landslides and debris flows/sediment transport areas caused by the Typhoons Aere in 2004 and Matsa in 2005. For both events, pre- and post-disaster optical satellite images (SPOT-5 with 2.5 m spatial resolution) were analysed. A Digital Elevation Model (DEM) with 5 m spatial resolution and its derived products, i.e., slope and curvature, were additionally integrated in the analysis to support the semi-automated object-based landslide mapping. Changes were identified by comparing the normalised values of the Normalized Difference Vegetation Index (NDVI) and the Green Normalized Difference Vegetation Index (GNDVI) of segmentation-derived image objects between pre- and post-event images and attributed to landslide classes.

scholarly journals Does Ecological Water Replenishment Help Prevent a Large Wetland from Further Deterioration? Results from the Zhalong Nature Reserve, China

2020 ◽  
Vol 12 (20) ◽  
pp. 3449
Author(s):  
Liwen Chen ◽  
Sixin Liu ◽  
Yanfeng Wu ◽  
Y. Jun Xu ◽  
Shengbo Chen ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Remote Sensing Data ◽  
Flood Pulse ◽  
Water Area ◽  
Vegetation Growth ◽  
Hydrological Conditions ◽  
Inundation Frequency ◽  
Inundation Area ◽  
Zhalong Wetland

Ecological water replenishment (EWR) has been increasingly applied to the restoration and maintenance of wetland hydrological conditions across China since the beginning of the 21st century. However, little is known about whether EWR projects help protect and/or restore wetland ecohydrology. As one of the earliest and longest-running EWR projects in China, water has been released from the Nenjiang River into the Zhalong wetland since 2001. It is important to examine the ecohydrological effects of this EWR project. In this study, long time series remote sensing data were used to extract the water area, inundation frequency, and normalized difference vegetation index (NDVI) to explore how eco-hydrological conditions changed during the pre- (1984–2000) and post-EWR (2001–2018) periods in the Zhalong wetland. Results show that the inundation area decreased due to the reduced surface water inflow during the pre-EWR period. Similarly, monthly vegetation NDVI in the growing season generally exhibited a decreasing and an increasing trend during the pre- and post-EWR periods, respectively. In the post-EWR period, NDVI increased by 19%, 73%, 45%, 28%, 13% for the months of May through September, respectively. Due to EWR, vegetation growth in areas with low inundation frequency was better than in areas with high inundation frequency. We found that the EWR project, runoff, and precipitation contributed 25%, 11%, and 64% to changes in the NDVI, respectively, and 46%, 37%, and 17% to changes in inundation area, respectively. These results indicate that the EWR project has improved hydrological conditions in the Zhalong wetland. For further maximum benefits of EWR in the Zhalong wetlands, we suggest that implementing similar eco-hydrological projects in the future should focus on flood pulse management to increase the inundation area, improve hydrological connectivity, and create new habitats.

scholarly journals Comparative Analysis of Landsat-8, Sentinel-2, and GF-1 Data for Retrieving Soil Moisture over Wheat Farmlands

2020 ◽  
Vol 12 (17) ◽  
pp. 2708 ◽  
Author(s):  
Qi Wang ◽  
Jiancheng Li ◽  
Taoyong Jin ◽  
Xin Chang ◽  
Yongchao Zhu ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Optical Sensors ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Cloud Model ◽  
Vegetation Indices ◽  
Optical Data ◽  
Landsat 8 ◽  
Different Sources ◽  
Sentinel 2

Soil moisture is an important variable in ecological, hydrological, and meteorological studies. An effective method for improving the accuracy of soil moisture retrieval is the mutual supplementation of multi-source data. The sensor configuration and band settings of different optical sensors lead to differences in band reflectivity in the inter-data, further resulting in the differences between vegetation indices. The combination of synthetic aperture radar (SAR) data with multi-source optical data has been widely used for soil moisture retrieval. However, the influence of vegetation indices derived from different sources of optical data on retrieval accuracy has not been comparatively analyzed thus far. Therefore, the suitability of vegetation parameters derived from different sources of optical data for accurate soil moisture retrieval requires further investigation. In this study, vegetation indices derived from GF-1, Landsat-8, and Sentinel-2 were compared. Based on Sentinel-1 SAR and three optical data, combined with the water cloud model (WCM) and the advanced integral equation model (AIEM), the accuracy of soil moisture retrieval was investigated. The results indicate that, Sentinel-2 data were more sensitive to vegetation characteristics and had a stronger capability for vegetation signal detection. The ranking of normalized difference vegetation index (NDVI) values from the three sensors was as follows: the largest was in Sentinel-2, followed by Landsat-8, and the value of GF-1 was the smallest. The normalized difference water index (NDWI) value of Landsat-8 was larger than that of Sentinel-2. With reference to the relative components in the WCM model, the contribution of vegetation scattering exceeded that of soil scattering within a vegetation index range of approximately 0.55–0.6 in NDVI-based models and all ranges in NDWI1-based models. The threshold value of NDWI2 for calculating vegetation water content (VWC) was approximately an NDVI value of 0.4–0.55. In the soil moisture retrieval, Sentinel-2 data achieved higher accuracy than data from the other sources and thus was more suitable for the study for combination with SAR in soil moisture retrieval. Furthermore, compared with NDVI, higher accuracy of soil moisture could be retrieved by using NDWI1 (R2 = 0.623, RMSE = 4.73%). This study provides a reference for the selection of optical data for combination with SAR in soil moisture retrieval.

scholarly journals Multiscaling NDVI Series Analysis of Rainfed Cereal in Central Spain

2021 ◽  
Vol 13 (4) ◽  
pp. 568
Author(s):  
David Andrés Rivas-Tabares ◽  
Antonio Saa-Requejo ◽  
Juan José Martín-Sotoca ◽  
Ana María Tarquis
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Agricultural Land ◽  
Vegetation Indices ◽  
Precipitation Pattern ◽  
Scaling Properties ◽  
Vegetation Growth ◽  
Factors Affecting ◽  
Series Analysis ◽  
Climate Interactions

Vegetation indices time series analysis is increasingly improved for characterizing agricultural land processes. However, this is challenging because of the multeity of factors affecting vegetation growth. In semiarid regions the rainfall, the soil properties and climate are strongly correlated with crop growth. These relationships are commonly analyzed using the normalized difference vegetation index (NDVI). NDVI series from two sites, belonging to different agroclimatic zones, were examined, decomposing them into the overall average pattern, residuals, and anomalies series. All of them were studied by applying the concept of the generalized Hurst exponent. This is derived from the generalized structure function, which characterizes the series’ scaling properties. The cycle pattern of NDVI series from both zones presented differences that could be explained by the differences in the climatic precipitation pattern and soil characteristics. The significant differences found in the soil reflectance bands confirm the differences in both sites. The scaling properties of NDVI original series were confirmed with Hurst exponents higher than 0.5 showing a persistent structure. The opposite was found when analyzing the residual and the anomaly series with a stronger anti-persistent character. These findings reveal the influences of soil–climate interactions in the dynamic of NDVI series of rainfed cereals in the semiarid.

Effect of Locust Invasion and Mitigation Using Remote Sensing Techniques: A Case Study of North Sindh Pakistan

2022 ◽  
Vol 88 (1) ◽  
pp. 47-53
Author(s):  
Muhammad Nasar Ahmad ◽  
Zhenfeng Shao ◽  
Orhan Altan
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Remote Sensing Data ◽  
Google Earth ◽  
Landsat 8 ◽  
Abrupt Decrease ◽  
Computing Platform ◽  
Remote Sensing Techniques ◽  
Processing Techniques

This study comprises the identification of the locust outbreak that happened in February 2020. It is not possible to conduct ground-based surveys to monitor such huge disasters in a timely and adequate manner. Therefore, we used a combination of automatic and manual remote sensing data processing techniques to find out the aftereffects of locust attack effectively. We processed MODIS -normalized difference vegetation index (NDVI ) manually on ENVI and Landsat 8 NDVI using the Google Earth Engine (GEE ) cloud computing platform. We found from the results that, (a) NDVI computation on GEE is more effective, prompt, and reliable compared with the results of manual NDVI computations; (b) there is a high effect of locust disasters in the northern part of Sindh, Thul, Ghari Khairo, Garhi Yaseen, Jacobabad, and Ubauro, which are more vulnerable; and (c) NDVI value suddenly decreased to 0.68 from 0.92 in 2020 using Landsat NDVI and from 0.81 to 0.65 using MODIS satellite imagery. Results clearly indicate an abrupt decrease in vegetation in 2020 due to a locust disaster. That is a big threat to crop yield and food production because it provides a major portion of food chain and gross domestic product for Sindh, Pakistan.

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 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.

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