scholarly journals Vegetation indices mapping for Bhiwani district of Haryana (India) through LANDSAT-7ETM+ and remote sensing techniques

2015 ◽  
Vol 7 (2) ◽  
pp. 874-879 ◽  
Author(s):  
A. Bala ◽  
K. S. Rawat ◽  
A. Misra ◽  
A. Srivastava
Keyword(s):  
Remote Sensing ◽  
Vegetation Indices ◽  
Condition Index ◽  
Wheat Crop ◽  
Agricultural Planning ◽  
Vegetation Condition Index ◽  
Regional Basis ◽  
Vegetation Health ◽  
Remote Sensing Techniques ◽  
Temporal Coverage

This study describes the VIs Vegetation Condition Index in term of vegetation health of wheat crop; with help of LANDSAT-7ETM+ data based NDVI and LAI for Bhiwani District of Haryana states (India) and gave the spatial development pattern of wheat crop in year 2005 over the study area of India. NDVI is found to vary from 0.3 to 0.8. In northern and southern parts of study area NDVI varied from 0.6 to 0.7 but in western part of Bhiwani showed NDVI 0.2 to 0.4 due to fertility of soil and well canal destitution. LAI showed variation from 1 to 6 accordingto the health of crop as the same manner of NDVI because LAI VI is NDVI dependent only change the manner of representation of vegetation health, due to this fact relation curve (r2=) between NDVI and LAI of four different growing date of sates are in successively increasing order 0.509, 0.563, 0.577 and 0.719. The study reveals that VIs can be mapped with LANDSAT-7ETM+ through remote sensing, which can be further used for many studies like crop yield or estimating evaptranspiration on regional basis for water management because satellite observations provide better spatial and temporal coverage, the VIs based system will provide efficient tools for monitoring health of crop for improvement of agricultural planning. VIs based monitoring will serve as a prototype in the other parts of the world where ground observations are limited or not available.

Estimating Area Coverage and Volume of Hot Lava by Integrating Multiple Satellite Remote Sensing Techniques

2021 ◽  
Author(s):  
Ninad Bhagwat ◽  
Xiaobing Zhou ◽  
Jiaqing Miao
Keyword(s):  
Remote Sensing ◽  
Rift Zone ◽  
Cost Effective ◽  
Interferometric Synthetic Aperture Radar ◽  
Areal Coverage ◽  
Volcanic Processes ◽  
Distribution Index ◽  
Remote Sensing Techniques ◽  
Temporal Coverage ◽  
Area And Volume

<p>Monitoring the regions that are prone to natural hazards is essential in disaster management, since early warnings can be issued. Airborne and space-borne remote sensing techniques are cost-effective in accomplishing the task. Estimating the area and volume of erupted lava can help researchers understand the volcanic processes and impact on land use and land cover. In this study, we developed a new algorithm to estimate areal coverage and volume of exposed hot lava by integrating the space-borne Interferometric Synthetic Aperture Radar (InSAR), thermal infrared, and Normalized Vegetation Distribution Index (NDVI) techniques. We applied this algorithm to the eruption of the East Rift Zone (ERZ) of the Kilauea volcano took place between May and August 2018 and estimated the areal coverage and volume of lava erupted. We compared the results of InSAR to those derived from airborne Light Detection and Ranging (LiDAR), and found that although air-borne LiDAR provides data with higher resolution and accuracy, InSAR is almost as good as LiDAR in monitoring deformed areas and has larger spatial and temporal coverage.</p>

scholarly journals Ecosystem Drought Response Timescales from Thermal Emission versus Shortwave Remote Sensing

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Erika Andujar ◽  
Nir Y. Krakauer ◽  
Chuixiang Yi ◽  
Felix Kogan
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Thermal Emission ◽  
Plant Stress ◽  
Meteorological Drought ◽  
Condition Index ◽  
Vegetation Health ◽  
Vegetation Health Index

Remote sensing is used for monitoring the impacts of meteorological drought on ecosystems, but few large-scale comparisons of the response timescale to drought of different vegetation remote sensing products are available. We correlated vegetation health products derived from polar-orbiting radiometer observations with a meteorological drought indicator available at different aggregation timescales, the Standardized Precipitation Evapotranspiration Index (SPEI), to evaluate responses averaged globally and over latitude and biome. The remote sensing products are Vegetation Condition Index (VCI), which uses normalized difference vegetation index (NDVI) to identify plant stress, Temperature Condition Index (TCI), based on thermal emission as a measure of surface temperature, and Vegetation Health Index (VHI), the average of VCI and TCI. Globally, TCI correlated best with 2-month timescale SPEI, VCI correlated best with longer timescale droughts (peak mean correlation at 13 months), and VHI correlated best at an intermediate timescale of 4 months. Our results suggest that thermal emission (TCI) may better detect incipient drought than vegetation color (VCI). VHI had the highest correlations with SPEI at aggregation times greater than 3 months and hence may be the most suitable product for monitoring the effects of long droughts.

scholarly journals Long Term Vegetation Health Monitoring in Dibru-Saikhowa National Park using Remote Sensing & Gis

2020 ◽  
Vol 9 (4) ◽  
pp. 1901-1906
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Vegetation Dynamics ◽  
National Park ◽  
Condition Index ◽  
Temperature Pattern ◽  
Positive Trend ◽  
Study Results ◽  
Wide Range ◽  
Vegetation Health

Remote sensing and GIS based vegetation monitoring offers lot of potential for ecosystem studies. This study utilized freely available moderate resolution Landsat images to quantify the changes in vegetation dynamics in Dibru-Saikhowa national park, India. A wide range of vegetation indices and temperature indices such as normalized difference vegetation index (NDVI), land surface temperature (LST), vegetation condition index (VCI), temperature condition index (TCI) and vegetation health index (VHI) was utilized for the purpose of the study. Results reveal that the study area has gone through changes in vegetation and temperature pattern affecting the land surface balances. The maximum NDVI value for the year 1996 was recorded between 0.5-0.8 whereas the maximum LST values ranged between 17.240C-34.850C. In 2019, the maximum NDVI values reduced to the range of 0.14-0.6 while LST increased to 18.950C-38.910C. Consequently, the VHI classes showed a negative trend. In 1996, healthy vegetation covered a total area of 14564.6 ha which reduced to 9872.1 ha in 2019. Conversely, the no vegetation class showed a significant positive trend from 951.3 ha to 3015.99. Such alteration in vegetation dynamics in the study area is affecting the local climate and regional ecosystem services and require instant attention of conservationist and policy makers

Airborne Hyperspectral Imaging for Monitoring Geothermal Activity Through Vegetation

2021 ◽  
Author(s):  
Cecilia Rodriguez-Gomez ◽  
Gabor Kereszturi ◽  
Robert Reeves ◽  
Andrew Rae ◽  
Reddy Pullanagari ◽  
...  
Keyword(s):  
Remote Sensing ◽  
New Zealand ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Spectral Response ◽  
Vegetation Indices ◽  
Thermal Infrared ◽  
Geothermal Activity ◽  
Vegetation Health ◽  
Infrared Data

<p>Remote sensing techniques are used to explore geothermal areas. They can offer spatial, temporal and spectral information to map lithological boundaries and hydrothermal alteration in a fast and cheap manner. However, some geothermal areas are densely covered by vegetation, which can hamper remote sensing monitor efforts for geothermal areas.</p><p>Vegetation cover in geothermal areas can reflect the subsurface activity, reacting to interactions between soil’s chemical conditions, heat and gas emissions. An example of such is kanuka (i.e. kunzea ericoides), an endemic shrub of geothermal areas in the Taupo Volcanic Zone (TVZ), New Zealand, which has been used as an indicator species for ground-based geothermal studies. This study assesses the use of airborne hyperspectral and thermal data over the Waiotapu Geothermal Field, TVZ, New Zealand, analysing kanuka shrub surface cover and its spectral response to geothermal activity. To explore the capability in hyperspectral remote sensing for geothermal site mapping and exploration, a series of vegetation indices, including; Anthocyanin Reflectance Index, Atmospherically Resistant Vegetation Index, Moisture Stress Index, Normalised Difference Vegetation Index, Simple Ratio Index, Vogelmann Index and Water Band Index were calculated from narrow bandwidth high-resolution hyperspectral.</p><p>The spectral response of vegetation was then analysed to explore the effects of geothermal heat, offering surrogate information on vegetation health. Vegetation indices results were compared against the thermal infrared data by visual interpretation and quantitative analyses, which shows strong spatial correlation among the vegetation cover type and heat distribution. Furthermore, exponential trendlines produced the best fit between vegetation indices and thermal infrared data. This correlation indicates soil temperatures affect the vegetation health (e.g. chlorophyll concentrations, newly forming leaves, water content). This relationship can highlight that there is valuable information in airborne hyperspectral data to complement exploration efforts, such as heat flux mapping. We conclude kanuka shrub has the potential to be employed as a proxy in exploration and monitoring of geothermal areas in New Zealand from remote sensing platforms.</p>

scholarly journals Comparative Analysis on Applicability of Satellite and Meteorological Data for Prediction of Malaria in Endemic Area in Bangladesh

2010 ◽  
Vol 2010 ◽  
pp. 1-8
Author(s):  
Atiqur Rahman ◽  
Leonid Roytman ◽  
Mitch Goldberg ◽  
Felix Kogan
Keyword(s):  
Malaria Incidence ◽  
Meteorological Data ◽  
Condition Index ◽  
Climate Data ◽  
Moisture Condition ◽  
Vegetation Condition Index ◽  
Trend Assessment ◽  
Weather Stations ◽  
Vegetation Health ◽  
Estimate Prevalence

Relationships between yearly malaria incidence and (1) climate data from weather station and (2) satellite-based vegetation health (VH) indices were investigated for prediction of malaria vector activities in Bangladesh. Correlation analysis of percent of malaria cases with Advanced Very High Resolution Radiometer- (AVHRR-) based VH indices represented by the vegetation condition index (VCI—moisture condition) and the temperature condition index (TCI—estimates thermal condition) and with rainfall, relative humidity, and temperature from ground-based meteorological stations. Results show that climate data from weather stations are poorly correlated and are not applicable to estimate prevalence in Bangladesh. The study also has shown that AVHRR-based vegetation health (VH) indices are highly applicable for malaria trend assessment and also for the estimation of the total number of malaria cases in Bangladesh for the period of 1992–2001.

scholarly journals Spectral and Numerical Analysis of Hyper spectral Data using Vegetation Indices

2019 ◽  
Vol 8 (6) ◽  
pp. 2156-2162
Keyword(s):  
Remote Sensing ◽  
Agricultural Research ◽  
Numerical Study ◽  
Vegetation Indices ◽  
Infrared Region ◽  
Misclassification Rate ◽  
Tomato Crop ◽  
Random Forest Classification ◽  
Forest Classification ◽  
Remote Sensing Techniques

Indian economy is majorly influenced by Agriculture and its allied sectors. More than 50% of the population of India is dependent on agriculture and its allied sectors for their survival. According to the Report of Indian Council of Agricultural Research, 30-35% of the crop yield gets wasted due to disease. Using modern day remote sensing techniques plant health can be monitored and it can be specified whether a plant is diseased or healthy. Hyperspectral Remote Sensing is the technique by which fine and minute information of vegetation can be obtained with the help of narrow wavebands. Data of 80 leaf samples of Tomato crop collected in spectra form and text form using ASD FieldSpec4 spectroradiometer and ViewSpec PRO. This information of plant leaves was used to identify vegetation attributes and its status. Vegetation Indices are calculated using mathematical formulae published in the previous study. Random forest classification used to discriminate among Healthy and Diseased plants. Algorithm works with an accuracy of 93.75% with misclassification rate 0.0625. Along with Green wavelength range and Red edge of the spectrum, specific disrupted behavior was observed in Shortwave Infrared Region of the spectra. The research paper focuses on Spectral and Numerical study and analysis of Tomato Leaf disease with the help of ASD FieldSpec4.

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 Use of aerial image in the estimation of volume and biomass of Eucalyptus sp. forest stand

2020 ◽  
pp. 286-294
Author(s):  
Thallita R. S. Mendes ◽  
Eder P. Miguel ◽  
Pedro G. A. Vasconcelos ◽  
Marco B. X. Valadão ◽  
Alba V. Rezende ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Basal Area ◽  
Aerial Image ◽  
Biomass Estimation ◽  
Fit Statistics ◽  
Remote Sensing Techniques ◽  
The Individual

Assessing forest stands is crucial for managing and planning the use of these resources. Forest inventory is the instrument that provides information about the stand situation, which can be costly and time consuming. In order to facilitate and reduce the time spent obtaining these data, the main objective of this work was to evaluate the accuracy of volume and biomass estimates per unit area with data from remote sensing. Forty sample units were allocated and georeferenced, in which all trees with diameter at breast height (DBH) ≥ 5 cm were inventoried. Sequentially, the cubage was performed in order to obtain individual biomass, volume, and adjustment of the individual models. With data from georeferenced images of the study area, the vegetation indices MSAVI (Modified Soil-Adjusted Vegetation Index) and NDVI (Normalized Difference Vegetation Index) were obtained. The volume and biomass estimation using remote sensing variables were carried out through the adjustment of sigmoidal models by regression analysis, which used a combination of the average values of the vegetation indices and the basal area of the plot/hectares as an independent variable. The fit statistics and the accuracy of the tested models presented consistent results to estimate forest production. The results showwd that indices derived from remote sensing techniques associated with forest variables information could accurately estimate the volume and biomass of Eucalyptus spp. plantations.

A novel agricultural drought monitoring framework using remote sensing products

2021 ◽  
Author(s):  
Trupti Satapathy ◽  
Meenu Ramadas ◽  
Jörg Dietrich
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Water Stress ◽  
Land Surface ◽  
Condition Index ◽  
Agricultural Drought ◽  
Drought Monitoring ◽  
Crop Water ◽  
Vegetation Health ◽  
Crop Water Stress

<p>Among natural hazards, droughts are known to be very complex and disastrous owing to their creeping nature and widespread impacts. Specifically, the occurrence of agricultural droughts poses a threat to the productivity and socio-economic development of countries such as India. In this study, we propose a novel framework for agricultural drought monitoring integrating the different indicators of vegetation health, crop water stress and soil moisture, that are derived from remote sensing satellite data. The drought monitoring is performed over Odisha, India, for the period 2000-2019. Soil moisture and land surface temperature datasets from GLDAS Noah Land Surface Model and surface reflectance data from MODIS (MOD09GA) are used in this study. We compared the utility of popular indices: (i) soil moisture condition index, soil moisture deficit index and soil wetness deficit index to represent the soil moisture level; (ii) temperature condition index, vegetation condition index and normalised difference water index to indicate vegetation health; (iii) short wave infrared water stress to represent crop water stress condition. Correlation analyses between these indices and the seasonal crop yields are performed, and suitable indicators are chosen. The popular entropy weight method is then used to integrate the indices and develop the proposed composite drought index. The index is then used for monitoring the agricultural drought condition over the study area in drought periods. The proposed framework for week- to month-scale monitoring have potential applications in identification of agricultural drought hotspots, analysis of trends in drought severity, and drought early warning for agricultural water management.</p>

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