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Mapping Tillage Practices Using Spatial Information Techniques | SpringerLink
Mar 14, · Spectral mixture analysis method. In satellite images, due to the high variability in the distribution of land-cover components, the pixels usually contain mixed spectral information. Spectral mixture analysis is based on this concept that pixel spectral response is a function of the weighted average of the objects within it Nov 20, · The aim of the present research is to monitor changes in herbage production during the grazing season in the Semirom and Brojen regions, Iran, using multitemporal Moderate Resolution Imaging Spectroradiometer (MODIS) data. At first, various preprocessing steps were applied to a topography map. The atmospheric and topographic corrections were applied using subtraction of the May 16, · Spectral mixture analysis. SMA is basically a physically-based image-processing tool aiding in precise repeated derivation of quantitative subpixel information (Roberts et al., ; Smith et al., ). SMA works under the assumption that a spectrum computed by a sensor is considered as a linear combination of the spectra of all the Snow: NDVI , Rμm >
Spectral mixture analysis and rangeland monitoring + sciencedirect+free download
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Download Free PDF. Multi-scale standardized spectral mixture models. Cristina Milesi. Download PDF Download Full PDF Package This paper.
A short summary of this paper. Remote Sensing of Environment — Contents lists available at SciVerse ScienceDirect Remote Sensing of Environment spectral mixture analysis and rangeland monitoring + sciencedirect+free download homepage: www. By Received 19 January combining the benefits of location-specific mixture models with standardized spectral indices, spectral mixture analysis and rangeland monitoring + sciencedirect+free download, standardized Received in revised form 19 May mixture models offer consistency, simplicity, inclusivity and applicability.
Global composites of , Landsat spectra, constructed from both Keywords: exoatmospheric reflectance and atmospherically corrected surface reflectance, represent the spectral diversity Spectral mixture model of a wide range of terrestrial environments.
Suites of individual substrate, Vegetation index vegetation and dark endmember spectra are used to derive mean endmembers and to quantify the effects of endmember variability on fractions estimated from a standardized Substrate, Vegetation, and Dark SVD linear mixture model.
Maximum endmember variability introduces less than 0. The mean SVD endmembers define a standard global mixture model for Landsat spectra. Substrate fractions do not scale as linearly for the urban validation sites because the Landsat substrate endmember does spectral mixture analysis and rangeland monitoring + sciencedirect+free download accurately represent the impervious surfaces imaged by WorldView Comparisons of Landsat and WorldView-2 unmixed with the same Visible-Near Infrared VNIR endmembers derived from the global Landsat endmembers are also strongly correlated but with reduced bias.
Comparisons of vegetation fractions with vegetation indices for the global composite show strong linear correspondence for Tasseled Cap Greenness and Enhanced Vegetation Index, with some degree of saturation at high fractions for the Soil Adjusted Vegetation Index and a wide range of responses for the Normalized Difference Vegetation Index.
Introduction estimates of the areal abundance of specific land cover types endmembers contributing to the mixed pixel Adams et al. By representing each ments as linear mixtures of endmember radiances reflected from differ- pixel as a combination of endmembers, spectral mixture analysis and rangeland monitoring + sciencedirect+free download, the resulting fraction images ent materials in the sensor's Instantaneous Field of View IFOV.
In cases provide continuous field representations of the spectrally heterogeneous of homogeneous target spectra, these endmembers are often considered gradations in land cover that characterize much of the Earth surface.
Inverting the linear mixture model yields a homogeneous thematic land cover class with discrete boundaries. tent definitions of thematic classes than may be obtained from statistical E-mail address: csmall columbia.
edu C, spectral mixture analysis and rangeland monitoring + sciencedirect+free download. classification methods. Small, C. The number and choice of endmembers are with other standardized vegetation metrics over a wide range of differ- the defining characteristics of the model. However, it is also possible to ent environments, spectral mixture analysis and rangeland monitoring + sciencedirect+free download.
As the basis for the analysis we use a global compos- use the linear mixture model as a more general representation of land ite of spectrally diverse subscenes collected by Landsat 5 and cover by using generic endmembers representative of common land Landsat 7.
The images are generic spectral endmembers, as a standardized spectral mixture calibrated to both exoatmospheric reflectance Chander et al. A standardized spectral mixture model can offer many of the and surface reflectance Masek et al. To accomplish these objectives we first simple, physically-based representation of the abundance of different construct a global composite from the subscenes and select suites materials within the IFOV.
The implicit assumption is that non-linear of Substrate, Vegetation, spectral mixture analysis and rangeland monitoring + sciencedirect+free download Dark SVD endmembers spanning its mixing e. In order to be gen- 3D mixing space. We use these endmember suites to quantify the effect erally applicable, the standardized spectral mixture model must repre- of endmember variability on the Spectral mixture analysis and rangeland monitoring + sciencedirect+free download endmember fractions estimated sent the diversity of materials likely to be imaged at different locations for the global Landsat composite.
We investigate the linearity of spatial and times. This means that the number and choice of generic scaling by comparing endmember fractions derived from Landsat with endmembers that define the model must encompass the range of re- fractions derived from near simultaneous acquisitions of WorldView flectances that can be distinguished by the sensor.
The standardized Finally, we compare vegetation fractions estimated with the generic spectral mixture model does not imply that the endmembers used are endmembers to Tasseled Cap greenness and three vegetation indices the only spectrally distinct i. It merely represents the mixed reflectance measurement as the a wide variety of environments. combination of generic endmember fractions that most closely matches This study uses the analysis of Small b as a starting point and the measurement. The standardized mixture model is not intended to extends the analysis in five ways.
yses are conducted using both exoatmospheric top of atmosphere and A standardized spectral mixture model can be thought of as an alter- atmospherically corrected surface reflectance. endmembers representing the most spectrally distinct land cover com- 5 Vegetation fraction estimates from the standardized model are com- ponents that the sensor can resolve Small, b. by Adams and Gillespie for specific types of scenes — but are fur- ther generalized to represent the diversity of spectral mixtures that can 2.
Data be resolved by a given sensor over the full range of landscapes found on Earth. Another benefit is the ability spectral mixture analysis and rangeland monitoring + sciencedirect+free download of diversity of land cover and diversity of biomes Fig. The global col- represent landscapes as continuous fields of fundamental land cover lection spans all terrestrial biomes as determined by mean annual tem- components.
The standardized spectral mixture model has its concep- perature and precipitation Houghton et al. We also convert the data to surface used. Despite the conceptual similarity in their origins, there are impor- reflectance correcting for atmospheric effects by means of the 6S code tant distinctions between the standardized linear spectral mixture implementation in the Landsat Ecosystem Disturbance Adaptive Process- model and the Kauth—Thomas Tasseled Cap Transformation TCT as ing System LEDAPS atmospheric correction method Masek et al.
Ice sheets and open marine its distinction from the underlying physical concept of spectral mixing environments are not well represented in the collection because the at- in the radiance field raises two complementary points. endmembers that a particular sensor can distinguish do not necessarily In each scene we strive to use cloud-free imagery to the extent possible. encompass all spectrally distinct materials that might be considered The atmospheric correction reduces the perturbations caused by the endmembers for a different sensor capable of distinguishing more or Rayleigh scattering and the absorption of the mixing atmospheric mole- different spectra.
In this sense, the mixture model is sensor-specific. cules and aerosols Vermote et al. In the analyzed dataset, the 2 Different sensors with similar spectral responses can represent LEDAPS correction ledapsSrc.
This suggests that generic the effects of Rayleigh scattering at low reflectances of the visible endmembers derived from one sensor may provide a basis literally bands and increasing the reflectance in the SWIR, which is otherwise re- and mathematically for linear mixture models of spectra measured duced by aerosols and other gas molecules absorption Ju et al.
In by other sensors with similar spectral responses. In this sense, the our study the atmospheric correction has the effect of eliminating some mixture model and its canonical endmembers may be portable from of the random variations in the fractions that would otherwise appear one sensor to another.
from unmixing exoatmospheric reflectances. Geographic and climatic distributions of Landsat subscenes. Overall scene selection criteria favor spectral diversity resulting from land cover diversity across biomes. Subscene selection criteria favor within-scene spectral diversity and land cover transitions. Subscene sample coverage corresponds well to global land area distribution ex-Antarctica within the climatic parameter space from Small a. All biomes are represented although cloud cover limits data availability in higher precipitation regions.
Biome classification modified from Houghton et al. Ice sheets are omitted because of atmospheric correction limitations. of spectral diversity. The subscene collection includes all of 30 its spectral diversity and because we have two clear sky WV2 acquisi- subscenes used by Small b. For each reflectance product all tions coinciding with same day Landsat acquisitions. On the later acquisition, the WV2 view geometry was The global composite illustrates both the spectral diversity and very similar to that of Landsat 5 but on the earlier acquisition the eleva- the consistency of land cover as imaged by Landsat Fig.
When all tion angles differ by 17°. Analysis dominance of substrates brownvegetation green and water black is apparent. Shallow marine substrates, evaporates playas, dry lakes We use a Principal Component PC analysis to quantify the spec- and other surfaces containing partially hydrous precipitate depositsice tral dimensionality of the global composite and to render the mixing and snow all have comparatively high visible reflectance relative to IR space from which the endmembers are selected.
We infer spectral di- and therefore appear in shades of blue to cyan. is more apparent. After rendering the mixing space from the three www. low order PCs we identify a suite of candidate endmember spectra We test linearity of scaling by comparing 30 m Landsat fraction esti- from the apexes of the pixel cloud corresponding to substrates Smates from global endmembers with high resolution 2 m fraction esti- vegetation Vand dark surfaces D. We refer to the S, V, and D mates for two pairs of coincident Landsat and WorldView-2 WV2 endmembers as primary endmembers because they bound the vast acquisitions, spectral mixture analysis and rangeland monitoring + sciencedirect+free download.
λ μm SubstrateE VegetationE DarkE SubstrateS VegetationS DarkS 0. The names Substrate, Vegetation, and Dark are spectral mixture analysis and rangeland monitoring + sciencedirect+free download for brevity, spectral mixture analysis and rangeland monitoring + sciencedirect+free download. In reality, Substrate includes rock, spectral mixture analysis and rangeland monitoring + sciencedirect+free download, sediment, soil and non-photosynthetic vegetation NPV.
Vegetation refers to photosyn- thetic foliage characterized by chlorophyll absorptions in the visible and high reflectance in the NIR. The Dark endmember contains a fundamental ambiguity resulting from low surface reflectance and the dominance of atmospheric scattering. Dark targets may be either absorptive e. The reflectances used here are distinct from reflectivity in their dependence on solar illumination and surface roughness.
We also identify secondary endmembers representing spe- Fig. Eigenvalues of the cific deviations from the plane spanned by the S, V, and D endmembers global composite covariance matrix show similar variance distributions for 10 subsets of to illustrate the spectral character of these deviations.
Although the Landsat feature endmember linear mixture model to estimate Substrate, Vegetation, and space is 6 dimensional, the eigenstructure of the mixing space is effectively 3 dimensional. Dark fractions. The model is inverted using averaged endmembers as For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.
well as individual pixel endmember spectra to quantify the effects of endmember variability as described below. Within the set of constraint of equal weight to the band-specific fraction sum equations. convex hulls defined by these triplets of individual pixel spectra, the For each inversion the endmembers and the estimated endmember frac- pixel density of the mixing space increases and the similarity among ad- tions are used to forward model the mixed reflectances for comparison jacent pixel spectra increases Fig.
Where do qualitative assessments fit in an era of increasingly quantitative monitoring? - Nika Lepak
, time: 5:47Spectral mixture analysis and rangeland monitoring + sciencedirect+free download
SPIE Digital Library Proceedings. CONFERENCE PROCEEDINGS Mar 01, · Introduction. Assessing and monitoring the state of the earth surface is a key requirement for global change research (Committee on Global Change Research, National Research Council, ; Jung et al. ; Lambin et al. ).Classifying and mapping vegetation is an important technical task for managing natural resources as vegetation provides a base for all living beings and Apr 24, · Rangeland degradation has an impact on biotic sustainability and reduces the variety of future uses of rangeland ecosystems (Kauffman et al. ).Decisions taken about land use, the number of livestock species to use, the season of use and stocking density all have profound and far-reaching effects on the stability and, ultimately, the sustainable use of rangelands (Squires )
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