By Prasad S. Thenkabail
A quantity within the three-volume Remote Sensing instruction manual series, Land assets tracking, Modeling, and Mapping with distant Sensing records the clinical and methodological advances that experience taken position over the last 50 years. the opposite volumes within the sequence are Remotely Sensed info Characterization, category, and Accuracies, and Remote Sensing of Water assets, mess ups, and concrete Studies.
In precise guide sort, the chapters within the quantity were rigorously chosen, equipped, and designed to be self-contained that you should concentrate on a bankruptcy and skim it via with no need to be overly depending on different chapters. This quantity presents accomplished theoretical and functional assurance of distant sensing of land assets that come with crops and biomass, agricultural croplands, rangelands, phenology and foodstuff defense, forests, biodiversity, ecology, land use\land disguise, carbon, and soils.
- Global terrestrial carbon and carbon budgets
- Precision farming
- Agricultural platforms reviews and soil studies
- Global croplands, agricultural croplands, and rangelands
- Food defense analysis
- Land use and land conceal mapping
- Measuring photosynthesis from space
- Vegetation characterization and above flooring biomass measurements and modeling
- Hyperspectral distant sensing
- Ecological studies
- Tropical wooded area characterization
- Habitat mapping and monitoring
In a really useful method, the ebook demonstrates the event, application, tools, and versions utilized in learning a big selection of land purposes. thought of magnum opus at the topic, the three-volume Remote Sensing Handbook is edited through Dr. Prasad S. Thenkabail, a well known foreign professional in distant sensing technology, GIScience, and spatial sciences, with contributions from a few of the absolute best major worldwide specialists, the instruction manual offers a data base at the evolution distant sensing technology, state of the art know-how, and a destiny imaginative and prescient for the field.
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A quantity within the three-volume distant Sensing instruction manual sequence, Land assets tracking, Modeling, and Mapping with distant Sensing records the clinical and methodological advances that experience taken position over the past 50 years. the opposite volumes within the sequence are Remotely Sensed facts Characterization, category, and Accuracies, and distant Sensing of Water assets, mess ups, and concrete experiences.
Extra resources for Remote Sensing Handbook - Three Volume Set: Land Resources Monitoring, Modeling, and Mapping with Remote Sensing
1998; Zarco-Tejada et al. 2002). Ciganda et al. (2008) showed that for the same LAI amount, the chlorophyll content during the 13 Monitoring Photosynthesis from Space green-up stage might be more than two times higher than the chlorophyll content in leaves in the reproductive and senescence stages. In the G-R model, both fAPAR and LUE are driven by total chlorophyll content with strong correlations between GPP/ PAR and canopy chlorophyll content (Gitelson et al. 2006; Peng et al. 2011). Ma et al.
S. central Great Plains. 4 Relationship between biome averaged integrated NDVI from NOAA-AVHRR sensors and net primary productivity rates. N. ) the previous year integrated NDVI was well correlated with current year increases in tree height growth. The annual integrated VI offers a robust approximation of vegetation productivity, because, in general, VIs provide both a measure of the capacity to absorb light energy, as well as reflect recent environmental stress acting on the canopy, with stress forcings showing up as reductions in NDVI expressed as either less chlorophyll and/or less foliage (Running et al.
Olofsson et al. (2008) reported strong correlations between EVI and GPP across Northern Europe, while NDVI showed problems with saturation in such areas of high biomass. NDVI saturation is attributed to the strong weighing of the red band, which is primarily absorbed by the uppermost leaf layer of a dense crop or forest canopy while the nonabsorbing NIR band is able to penetrate 5–7 leaf layers. Thus, the more NIR-sensitive indices, such as EVI, PVI, TC-GVI, and linear mixture models are less prone to saturate (Huete et al.