IMPACT OF CLIMATE PARAMETERS ON VEGETATION USING DIFFERENT INDICES IN HARDIWAR DISTRCIT, INDIA
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Vegetation moisture is a key parameter in fire risk modeling. Many authors have demonstrated the role of remote sensing in the assessment of the equivalent water thickness (EWT), which is defined as the weight of liquid water per unit of leaf surface. However, forest fire danger models rely on fuel moisture content (FMC) as a measure of vegetation moisture. FMC is defined as the ratio of the leaf liquid water weight over the leaf dry weight. In a previous research it has been shown the potential of the Moderate Resolution Imaging Spectroradiometer (MODIS) ground reflectance data in retrieving both EWT and FMC at leaf level. Though the atmosphere alters ground signature recorded by the sensor, in this paper it will be shown that a simple index can be designed that allows a fast and accurate estimation of FMC (r 2 = 0.83) with MODIS data.
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The Moderate Resolution Imaging Spectroradiometer (MODIS) instrument is considered a very versatile tool in studying environmental changes. The multi-spectral sensor owns a high revisit period, a large scanning area, plus a handful of other advantages. The main purpose of this study is to employ reflectance data retrieved by the MODIS sensor in detecting smoke plumes, estimating their respective intensity and retrieving the AOD (Aerosol Optical Depth). Specifically, in the detection of the smoke plumes, biomass burning cases are studied in delineating the reflective characteristics. Following the detection, the Deep-Blue Aerosol Index (DAI) is utilized to evaluate the intensity. Relevant AOD information is retrieved by analyzing the relationship between the DAI and AOD. Results show a high correlation between the satellite-retrieved AOD and Sun Photometer-observed AOD data, thus demonstrating the feasibility in obtaining the aerosol distribution over highly reflective areas. As the proposed approach in this study is capable of accurately portraying the spatial distribution and intensity of smoke plumes, it can be effectively used in monitoring biomass burning hazards.
Moderate-resolution imaging spectroradiometer
Spectroradiometer
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This paper aims to explore the integration of field spectroscopy and satellite remote sensing approaches to detect underground structures in Cyprus. A SVC-HR1024 field spectroradiometer was used and in-band reflectances were determined for medium resolution Landsat 7 ETM satellite sensor. In order to study possible differences of the spectral signature of vegetation, Normalized Difference Vegetation Index (NDVI), Simple Ratio (SR) and Enhanced Vegetation Index (EVI) have been used for the detection of underground military structures. The simulation results show that Vegetation Indices are highly useful and extremely valuable for detection underground infrastructures in Cyprus. In this study, two test areas were identified, analyzed and modelled: Area (a) which is a Vegetation Area covered with vegetation (barley), in the presence of an underground military structure, and Area (b) which is a Vegetation Area covered with vegetation (barley), in the absence of an underground military structure.
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Vegetation plays a vital role in the ecological functioning of terrestrial and coastal ecosystems. Remote sensing generally provides timely and accurate information to manage ecosystems sustainably and effectively. In this respect, thermal infrared (TIR, 3–14 µm) remote sensing data form a valuable data source for vegetation studies. The TIR data provides unique information compared to other parts of the electromagnetic spectrum. This article aims to gather and review the most relevant information obtained using TIR remote sensing data for terrestrial vegetation at leaf and canopy levels using laboratory/field-based, airborne and spaceborne platforms. We address this topic from various angles, particularly focusing on vegetation discrimination as well as the quantification of water stress by means of canopy temperature and spectral emissivity. In addition, attempts to associate TIR spectral features with vegetation biochemical compounds, as well as the retrieval of vegetation biochemical and biophysical parameters, are reviewed. Research needs and requirements for successful use of remote sensing in vegetation studies across the TIR region, as well as significant challenges, are also discussed. Our review reveals that, despite the increasing interest among remote sensing experts in using TIR data, there are still large gaps in our understanding and interpretation of TIR imagery. Some inconsistent findings and contradictory observations have come to light in different levels (i.e., leaf and canopy levels). In addition, our review shows that airborne and TIR hyperspectral-based studies are currently limited due to cost, particularly across large spatial extents. It can be concluded that TIR remote sensing of vegetation offers unique insights in understanding terrestrial vegetation (e.g., vegetation water stress and retrieval of biophysical parameters). TIR is complementary to other remote sensing data sources, with a high potential for fusing data from different parts of the spectrum. However, we highlight challenges obtaining consistent, meaningful and accurate results for land surface temperature and land surface emissivity retrieval.
Thermal infrared
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Vegetation coverage is an important ecology parameter and used in many climatic and ecological models. Field measurement and remote sensing measurement are rudimental approaches to get vegetation coverage. As far as field measurement, currently common methods include sampling, instruments and visible estimating. Field measurement plays a crucial role in ground vegetation investigation, which provides a background for interpreting and quantifying remote sensing data. However, field measurement has much selflimitation, which does not satisfy exhibiting vegetation features and variation in a large area. Referring to remote sensing measurement, experiential models, subpixel models and vegetation indices approaches are three prime methods used for vegetation coverage estimation, which are restricted by some factors such as ground measurement precision and image spatial resolution. Vegetation indices mostly used in estimating vegetation coverage involve NDVI,ARVI,ASVI,GEMI,SAVI,MSAVI and SAVI, which have various suitable conditions. Corresponding to different spatial scales, actual remote sensing imagines can be divided into low spatial resolution images such as NOAA/AVHRR and MODIS, middling spatial resolution images such as TM,MSS and SPOT, and high spatial resolution images such as aerial photograph and IKONOS. Remote sensing measurement in grass vegetation coverage has close relation with field measurement data, so consummate design for field measurement is very essential for improving measuring precision of grassland vegetation coverage. Only fast combining these two kinds of data, we are having chances to get perfect grass vegetation coverage measuring results. This article aims at analyzing and discussing measurement of grassland vegetation coverage, synthetically studying methods of filed measurement and remote sensing measurement, and prospecting possible methods to improve measuring precision of grassland vegetation coverage. Undoubtedly, with the development and mature of sensor technology and various mathematics models, it is possible for us to get remote sensing imagine of high spatial resolution, great spatial scales and credible performance. Digital camera, hyperspectral remote sensing and comprehensive use of multiscale remote sensing data are possible development trends for improving measurement precision to grassland vegetation coverage. It is true that remote sensing imagine will play more and more important role in studying vegetation characters in the future.
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Remote sensing data obtained by airborne side-look radars over wide frequency ranges is analyzed. Relationships between backscattered signal and parameters of the investigated underlying surface have been derived. The classification of vegetation growth and forestry on test sites has been performed.
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Mapping vegetation through remotely sensed images involves various considerations, processes and techniques. Increasing availability of remotely sensed images due to the rapid advancement of remote sensing technology expands the horizon of our choices of imagery sources. Various sources of imagery are known for their differences in spectral, spatial, radioactive and temporal characteristics and thus are suitable for different purposes of vegetation mapping. Generally, it needs to develop a vegetation classification at first for classifying and mapping vegetation cover from remote sensed images either at a community level or species level. Then, correlations of the vegetation types (communities or species) within this classification system with discernible spectral characteristics of remote sensed imagery have to be identified. These spectral classes of the imagery are finally translated into the vegetation types in the image interpretation process, which is also called image processing. This paper presents an overview of how to use remote sensing imagery to classify and map vegetation cover. Specifically, this paper focuses on the comparisons of popular remote sensing sensors, commonly adopted image processing methods and prevailing classification accuracy assessments. The basic concepts, available imagery sources and classification techniques of remote sensing imagery related to vegetation mapping were introduced, analyzed and compared. The advantages and limitations of using remote sensing imagery for vegetation cover mapping were provided to iterate the importance of thorough understanding of the related concepts and careful design of the technical procedures, which can be utilized to study vegetation cover from remote sensed images.
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An outline is presented of the required spatial and spectral resolution needed for accurate vegetation discrimination and mapping studies as well as for determination of state of health (i.e., detection of stress symptoms) of actively growing vegetation. Good success was achieved in vegetation discrimination and mapping of a heterogeneous forest cover in the ridge and valley portion of the Appalachians using multispectral data acquired with a spatial resolution of 15 m (IFOV). A sensor system delivering 10 to 15 m spatial resolution is needed for both vegetation mapping and detection of stress symptoms. Based on the vegetation discrimination and mapping exercises conducted at the Lost River site, accurate products (vegetation maps) are produced using broad-band spectral data ranging from the .500 to 2.500 micron portion of the spectrum. In order of decreasing utility for vegetation discrimination, the four most valuable TM simulator VNIR bands are: 6 (1.55 to 1.75 microns), 3 (0.63 to 0.69 microns), 5 (1.00 to 1.30 microns) and 4 (0.76 to 0.90 microns).
VNIR
Vegetation Classification
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