Written by leading global experts, including pioneers in the field, the four-volume set on Hyperspectral Remote Sensing of Vegetation, Second Edition, reviews existing state-of-the-art knowledge, highlights advances made in different areas, and provides guidance for the appropriate use of hyperspectral data in the study and management of agricultural crops and natural vegetation. Volume IV, Advanced Applications in Remote Sensing of Agricultural Crops and Natural Vegetation discusses the use of hyperspectral or imaging spectroscopy data in numerous specific and advanced applications, such as forest management, precision farming, managing invasive species, and local to global land cover change detection. It emphasizes the importance of hyperspectral remote sensing tools for studying vegetation processes and functions as well as the appropriate use of hyperspectral data for vegetation management practices. The concluding chapter provides readers with useful guidance on the highlights and essence of Volume IV through the editors? perspective. Key Features of Volume IV: Guides readers to harness the capabilities of the most recent advances in applying hyperspectral remote sensing technology to the study of terrestrial vegetation.
Includes specific applications on agriculture, crop management practices, study of crop stress and diseases, crop characteristics based on inputs (e.g., nitrogen, irrigation), study of vegetation impacted by heavy metals, gross and net primary productivity studies, light use efficiency studies, crop water use and actual evapotranspiration studies, phenology monitoring, land use and land cover studies, global change studies, plant species detection, wetland and forest characterization and mapping, crop productivity and crop water productivity mapping, and modeling.
Encompasses hyperspectral or imaging spectroscopy data in narrow wavebands used across visible, red-edge, near-infrared, far-infrared, shortwave infrared, and thermal portions of the spectrum.
Explains the implementation of hyperspectral remote sensing data processing mechanisms in a standard, fast, and efficient manner for their applications.
Discusses cloud computing to overcome hyperspectral remote sensing massive big data challenges.
Provides hyperspectral analysis of rocky surfaces on the earth and other planetary systems.
Written by leading global experts, including pioneers in the field, the four-volume set on Hyperspectral Remote Sensing of Vegetation, Second Edition, reviews existing state-of- the-art knowledge, highlights advances made in different areas, and provides guidance for the appropriate use of hyperspectral data in the study and management of agricultural crops and natural vegetation. Volume II, Hyperspectral Indices and Image Classifications for Agriculture and Vegetation evaluates the performance of hyperspectral narrowband or imaging spectroscopy data with specific emphasis on the uses and applications of hyperspectral narrowband vegetation indices in characterizing, modeling, mapping, and monitoring agricultural crops and vegetation. This volume presents and discusses topics such as the non-invasive quantification of foliar pigments, leaf nitrogen concentration of cereal crop, the estimation of nitrogen content in crops and pastures, and forest leaf chlorophyll content, among others. The concluding chapter provides readers with useful guidance on the highlights and essence of Volume II through the editors? perspective. Key Features of Volume II: Provides the fundamentals of hyperspectral narrowband vegetation indices and hyperspectral derivative vegetation indices and their applications in agriculture and vegetation studies.
Discusses the latest advances in hyperspectral image classification methods and their applications.
Explains the massively big hyperspectral sensing data processing on cloud computing architectures.
Highlights the state-of-the-art methods in the field of hyperspectral narrowband vegetation indices for monitoring agriculture, vegetation, and their properties such as plant water content, nitrogen, chlorophyll, and others at leaf, canopy, field, and landscape scales.
Includes best global expertise on hyperspectral remote sensing of agriculture, crop water use, plant species detection, crop productivity and water productivity mapping, and modeling.
Hyperspectral Remote Sensing Of Vegetation Ebook Download
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Hyperspectral narrow-band (or imaging spectroscopy) spectral data are fast emerging as practical solutions in modeling and mapping vegetation. Recent research has demonstrated the advances in and merit of hyperspectral data in a range of applications including quantifying agricultural crops, modeling forest canopy biochemical properties, detecting
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Leaf chlorophyll content is not only an important biochemical parameter for determinating the capacity of rice photosynthesis, but also a good indicator of crop stress, nutritional state. Due to the reliable, operational and non-destructive advantages, hyperspectral remote sensing plays a significant role for assessing and monitoring chlorophyll content. In the study, a few of typical vegetation indices (VI) with the combination of 670nm and 800nm band reflectance, Normalized Difference Vegetation Index (NDVI), Modified Simple Ratio index (MSR), Modified Chlorophyll Absorption Ratio Index (MCARI), Transformed Chlorophyll Absorption Ratio Index (TCARI), and Optimized Soil-Adjusted Vegetation Index (OSAVI) are modified by using 705nm and 750nm band reflectance so as to reduce the effect of spectral saturation in 660-680nm absorptive band region, and then used to assess the rice chlorophyll content. The result shows that the five mentioned VIs have better correlation with rice chlorophyll content while using 705nm and 750nm. In addition, in the study the Weight optimization combination (WOC) principle is utilized to further assess the capacity of the five modified VIs for estimating rice chlorophyll content, it is proved that OSAVI and MSR display the better performance.
Biodiversity is critical to ecosystem function and provides many goods and services essential to human well-being (Hooper et al. 2012; Tilman et al. 2012). Despite centuries of effort, we lack a comprehensive account of global biodiversity, at a time the world is facing a sixth mass extinction due to human disturbance and climate change (Barnosky et al. 2011). Effective management of biological resources to preserve diversity and maintain ecosystem function in a rapidly changing world remains difficult, in part due to sampling challenges and lack of globally consistent data sets. Sampling biodiversity using traditional field methods alone simply cannot address this need, leading to recent calls for remote sensing (RS) as part of a global biodiversity monitoring system (Scholes et al. 2012; Pereira et al. 2013; Turner 2014; Jetz et al. 2016; Geller et al. Chap. 20).
With the advent of hyperspectral sensors and imaging spectrometers, a growing number of studies have utilized optical diversity, or the variability in vegetation optical properties (also called spectral diversity) to assess species diversity (typically alpha or beta diversity), or to address plant traits related to functional diversity. These methods offer the opportunity to directly detect species and functional diversity, but also require close attention to scale (Asner et al. 2015). In this chapter, our primary focus is on these latter RS methods involving optical RS, with the understanding that other RS methods can also make important contributions to our understanding of biodiversity.
The advent of hyperspectral sensors, both imaging and nonimaging, provides rich opportunities for exploring spectral features related to biodiversity. Approaches range from detection of species or functional traits to methods based on the information content of the spectra themselves (Fig. 16.1; Table 16.2). All of these methods require attention to spectral scale, including spectral resolution and range, which influence biodiversity detection in complex ways. Furthermore, our methods of analysis, ranging from simple vegetation indices to more complex full-spectral statistical methods (Table 16.2), explore the spectral dimension in different ways and to varying degrees. To date, relatively few studies have explicitly addressed spectral scale in the context of biodiversity detection, but most show that more spectral information is generally better than less (e.g., Asner et al. 2012). Consequently, hyperspectral sensors are more informative than multiband sensors, and full-range (VIS-SWIR) detectors are usually more useful than limited range (e.g., VIS-NIR) detectors for detecting plant traits or biodiversity. The importance of spectral scale in biodiversity detection can be readily seen when comparing multiband data (measured from a drone) to hyperspectral data (measured from a tram system) for Cedar Creek; in this case, multiband drone imagery failed to detect different alpha diversity levels, despite pixel sizes (2.3 cm) that were intermediate between those of the hyperspectral sensor (Fig. 16.5).
However, matching instrument to organizational scale remains a challenge; we typically have a poor match between the pixel size and sampling extent and the organism size and distribution on the ground, often due to the practical constraints of field sampling (in the case of biological studies) and instrument design (in the case of RS). Most remote sensing instruments are designed for a particular airborne or spaceborne platform with physics and engineering requirements in mind. The detector response is constrained by the amount of electromagnetic energy available, which in turn determines the sensor design, pixel size, and spectral resolution needed to achieve a given signal-to-noise ratio. Greater signal-to-noise ratios can be attained by reducing the spectral resolution (combining narrow bands into broadbands, e.g., via spectral binning), or by reducing spatial resolution (e.g., pixel binning), but these choices limit the ability to distinguish individuals, species, and functional types due to the degradation of spectral and spatial information. Orbital and altitudinal considerations also determine the pixel size obtainable from a particular sensor platform. Together, these constraints often reduce the ability to properly distinguish individual organisms or vegetation functional types. Adding to this mismatch, field sampling (including plot size, transect size, and location) is often limited by practical considerations of personnel, time, and budget and is rarely designed with RS in mind. 2ff7e9595c
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