Remote sensing is an important tool for studying patterns in surface processes on different spatiotemporal scales. However, differences in the spatiospectral and temporal resolution of remote sensing data as well as sensor-specific surveying characteristics very often hinder comparative analyses and effective up- and downscaling analyses. This paper presents a new methodical framework for combi...

Hyperspectral remote sensing images provide richer information about materials than that of multispectral images. The new larger data volumes of hyperspectral sensors bring new challenges for traditional image processing techniques. Therefore, conventional classification methods could fail without employing dimension reduction preprocessing. The dimensional reduction methods can be totally divi...

For much of the past decade, imaging spectrometry has been primarily a research tool. With the recent appearance of commercial airborne hyperspectral imaging systems, and the impending launch of satellite-based sensors, imaging spectrometry is poised to enter the mainstream of remote sensing. As hyperspectral images become commonly available, both applications and users of the imagery will beco...

man has always been curious about his natural environmental, and has always been trying to know it more. to do this, he has employed many different ways and has used different information. in the meantime, he has examined nature phenomena indirectly and using remote sensing. today, expert and groups of experts are using remote sensing documents and this has caused them to believe that remote se...

Interpretation of remote sensing reflectance from coastal waters at different wavelengths of light yields valuable information about water column constituents, which in turn, gives information on a variety of processes occurring in coastal waters, such as primary production, biogeochemical cycles, sediment transport, coastal erosion, and harmful algal blooms. The Hyperspectral Infrared Imager (...

Quantitative, spatially explicit estimates of canopy nitrogen are essential for understanding the structure and function of natural and managed ecosystems. Methods for extracting nitrogen estimates via hyperspectral remote sensing have been an active area of research. Much of this research has been conducted either in the laboratory, or in relatively uniform canopies such as crops. Efforts to a...

Chlorophyll-a (chl-a) is a central water quality parameter that has been estimated through remote sensing bio-optical models. This work evaluated the performance of three well established reflectance based bio-optical algorithms to retrieve chl-a from in situ hyperspectral remote sensing reflectance datasets collected during three field campaigns in the Funil reservoir (Rio de Janeiro, Brazil)....

Iron is an indicator for soil fertility and the usability of an area for cultivating crops. Remote sensing is the only suitable tool for surveying large areas at a high temporal and spatial interval, yet a relative high spectral resolution is needed for mapping iron contents with reflectance data. Sentinel-2 has several bands that cover the 0.9 μm iron absorption feature, while space-borne sens...

Hyperspectral image classification has become an important research topic in remote sensing. Because of high dimensional data, a special attention is needed dealing with spectral data; and thus, one of the research topics in hyperspectral image classification is dimension reduction. In this paper, a dimension reduction approach is presented for classification on hyperspectral images. Advantages...

Plant canopy nitrogen (N) is associated with ecosystem processes such as photosynthetic and aboveground net primary production, particularly in forested ecosystems. Sagebrush N is directly relatable to wildlife nutritional status and contributes to assessments of habitat quality, productivity, plant / soil water dynamics and controls on canopy photosynthesis. Hyperspectral remote sensing studie...