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3 edition of Use of Landsat images of vegetation cover to estimate effective hydraulic properties of soils found in the catalog.

Use of Landsat images of vegetation cover to estimate effective hydraulic properties of soils

Peter S. Eagleson

Use of Landsat images of vegetation cover to estimate effective hydraulic properties of soils

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Published by Department of Civil Engineering, Massachusetts Institute of Technology in Cambridge, Mass .
Written in English

  • Landsat satellites.

  • Edition Notes

    StatementPeter S. Eagleson, principal investigator and Michael F. Jasinski, research assistant.
    SeriesNASA-CR -- 183185., NASA contractor report -- NASA CR-183185.
    ContributionsJasinski, Michael F., United States. National Aeronautics and Space Administration.
    The Physical Object
    Pagination1 v.
    ID Numbers
    Open LibraryOL15291227M

    Actual evapotranspiration estimation for different land use and land cover in urban regions using Landsat 5 data Wenjuan Liu,a,b Yang Hong,b Sadiq Ibrahim Khan,b Mingbin Huang,a,c Baxter Vieux,b Semiha Caliskan,d and Trevor Groutb a Northwest A&F University, College of Resource and Environment, Yangling, Shaanxi Province, , China. Vegetation Indices • If Images transformed by vegetation indices are to be used for display purposes, the values will need to be scaled, giving -1 a value of 0 ranging to +1 at a value of (for NDVI) • This allows two images taken at different times to be analyzed together. Landsat Spectral Bands 1 Coastal water mapping, soil/vegetation discrimination, forest classification, man-made feature identification 2 Vegetation discrimination and health monitoring, man -made feature identification 3 Plant species identificat ion, man -made feature identification 4 Soil moisture monitoring, vegetation monitoring, water body discriminationFile Size: 64KB. The thermal‐IR data were used to assess four qualitative soil moisture conditions (water/very wet, wet, moist, and dry) within each land‐use category. Integration of Landsat thermal‐IR data with land‐use through GIS under certain conditions may be a useful technique for assessing regional soil moisture conditions, and further research.

    Products for Climatic Variation and Agricultural Measurements in Cholistan Desert Abstract - The Landsat ETM+ has shown great potential in Keywords: Cholistan desert, EVI, Landsat ETM+, MODIS, NDVI, vegetation phenology. I. Introductıon described with focus on the projection from the agricultural mapping and monitoring due to its advantagesFile Size: KB.

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Use of Landsat images of vegetation cover to estimate effective hydraulic properties of soils by Peter S. Eagleson Download PDF EPUB FB2

Get this from a library. Use of Landsat images of vegetation cover to estimate Use of Landsat images of vegetation cover to estimate effective hydraulic properties of soils book hydraulic properties of soils.

[Peter S Eagleson; Michael F Jasinski; United States. National Aeronautics and Space Administration.]. Get this from a library. Use of Landsat images of vegetation cover to estimate effective hydraulic properties of soils: addendum to final technical report of 1 August covering the period 1 August September [Peter S Eagleson; Michael F Jasinski; United States.

National Aeronautics and Space Administration.]. Map units in the ESA containing wet and saline soils were updated and refined using Landsat 7 imagery.

Five land-cover classes are related to dominant soil types that vary in soil wetness, salinity, calcium carbonate concentration and vegetation cover type. Supervised classification of the imagery was performed using the five land cover by: 1. Canopy cover, the percent of the soil surface covered by plant foliage, is an important indicator of stage of growth and crop water use in horticultural crops.

Methods of using remote sensors to determine canopy cover in major crops have been studied for years, but the studies have not included most horticultural crops. The infiltration rate was very rapid under green forest ( mm h −1) while in cropped soils, it was 67 mm/h [38].

The results from a study conducted by [39] on the influence of land use and. Based on the common conceptual model that unique soils are the products of unique sets of soil-forming factors, Landsat spectral data can represent environmental covariates for vegetation (e.g.

surface hydrology. Mappable areas of vegetation and soils, although irregular in shape, often occupy from several hundred acres to several square miles.

The dominant land use--ranching--has not appreciably altered the basic patterns of plant communities. The relatively uniform composition of surface sediments as well as stable vegetative. REMOTE SENS. ENVIRON. () Estimating Changes in Vegetation Cover over Time in Arid Rangelands Using Landsat MSS Data G.

Pickup, V. Chewings, and D. Nelson CSIRO Division of Wildlife and Ecology, Centre for Arid Zone Research, Alice Springs, Australia Changes in vegetation cover over time in arid rangelands can be used to monitor land Cited by: These land cover/use data sets were created by the Upper Midwest Environmental Sciences Center (UMESC) using data collected by a Landsat thematic mapper satellite.

A report on how these data were generated is available upon request, and the report's abstract is available online. The report's abstract is one of many available to view through the. test soils was reduced from % to %. These results indicate that soil background effects can be significant in Landsat data but can be reduced using site specific soft information.

Introduction The discrimination of vegetation from its underlying soil background plays an. based images of the Earth’s land surface, providing data that. serve as valuable resources for land use/land change research.

The data are useful to a number of applications including forestry, agriculture, geology, regional planning, and education. Landsat is a joint effort of the U.S.

Geological Survey (USGS). Landsat Imagery Sheds Light on Agricultural Water Use Source: Loura Hall, NASA Earth / NASA Spinoff Using infrared imagery captured by Landsat satellites and publicly available on the Internet through Google Earth Engine, EEFlux can quickly create maps of evapotranspiration, a way to measure how much water is being used.

remote sensing Article A New Concept of Soil Line Retrieval from Landsat 8 Images for Estimating Plant Biophysical Parameters Nima Ahmadian 1,*, José A. Demattê 2, Dandan Xu 3, Erik Borg 4 and Reinhard Zölitz 1 1 Faculty of Natural Science and Mathematics, Institute of Geography and Geology, University of Greifswald, GreifswaldGermany; [email protected] File Size: 2MB.

The article contains certain aspects regarding the use of multispectral images in analyzing the forest vegetation from Gura Râului area, Sibiu County. The paper presents the basic principles of processing multispectral images, how to conduct analysis of vegetation types and some aspects related to multispectral image georeferencing.

In the. But as Landsat data are usually available for several dates within a year, you might try to combine these dates. Except in very dry areas, there is often a time when the soils are covered by vegetation and easy to differentiate from buildings and roads.

Monitoring terrestrial vegetation cover condition is important to evaluate its current condition and to identify potential vulnerabilities. Due to simplicity and low cost, point intercept method has been widely used in evaluating grassland surface and quantifying cover conditions. Field-based digital photography method is gaining popularity for the purpose of cover Cited by: 2.

We used remotely sensed data from the Landsat-8 and WorldView-2 satellites to estimate vegetation burn severity of the Creek Fire on the San Carlos Apache Reservation, where wildfire occurrences affect the Tribe's crucial livestock and logging industries.

Accurate pre- and post-fire canopy maps at high (meter) resolution were created from World- View-2 data to generate Cited by: distribution of vegetation coverage, land surface temperature investigated, and the relationships among these factors are discussed. In the study LST values were derived from the thermal band of the Landsat 5 Thematic Mapper (TM).

Vegetation cover of the test area was derived from the near-infrared and red bands of the Landsat 5 TM by using File Size: KB. Structural and functional analyses of ecosystems benefit when high accuracy vegetation coverages can be derived over large areas.

In this study, we utilize IKONOS, Landsat 7 ETM+, and airborne scanning light detection and ranging (lidar) to quantify coniferous forest and understory grass coverages in a ponderosa pine (Pinus ponderosa) dominated ecosystem in.

the vegetative cover or C factor has been one of the most difficult to estimate over broad geographic areas. The C factor represents the effects of vegetation canopy and ground covers in reducing soil loss. Traditional methods for the extraction of vegetation. Eagleson, P. (), Feasibility of using Landsat images of vegetation cover to estimate effective hydraulic properties of soils, Massachusetts Institute of Technology, 20 pages.

Eagleson, P. (), Areal coverage of storm precipitation, Final Technical Report NAG6 pages. the calibration study, Landsat data and Land Systems maps for one square mile area in each sheet were used.

These calibration areas were representative of each of the predominant Great Group Chernozemic soils found in the Prairie Region of Canada (Table 1). For the verification study, Landsat data for.

Use of Landsat images of vegetation cover to estimate effective hydraulic properties of soils [microform Satellite technology: an introduction / Andrew F. Inglis; Broadcasting satellites at 12 GHz for region technical characteristics [microform] / Edward F.

Miller. Vegetation shows up best at near infrared (NIR) wavelengths so scientists often use false color NIR images to detect vegetation.

You can see a false color infrared image by setting the display properties so that band 4 is displayed as red, band 3 as green and band 2 as blue: Note how the vegetation shows up brightly in band 4 (red).File Size: 1MB. “Analysis of vegetation using spectral information - the use of indices derived from Landsat satellite images” – Altobelli Alfredo et al.

Soil Moisture Index (SMI) REFERENCES: “Satellite remote sensing applications for surface soil moisture monitoring” – Lingli WANG et al. “Assessment of soil moisture using Landsat.

Landsat ETM images are widely used to observe and model the biophysical characteristics of the land surface. In addition to the development of Land use/cover maps band 6 of the Landsat imagery is useful for deriving the surface temperature. Several researchers used the Landsat imagery to develop land use/cover images as well as temperature images.

The question is: which satellite out the Landsat TM (Thematic Mapper) and SPOT 5 would you use to map general extent of a km x km study area. My answer was that you would use the SPOT 5 as it is higher resolution and would let you get a finer extent vs the 30m resolution of the Landsat TM.

However the SPOT 5 has a small swath area so you. Results highlight the potential of SAR and optical satellite images to contribute to effective SM content detection in support of water resources management and precision agriculture.

Keywords: Sentinel-1, Landsat 8, Soil moisture content, Artificial Neural Network, Multiple Linear Regression The study was fully supported by the CASCADE project. calculated from the Landsat 5 TM bands in order to compute the fraction of vegetation cover.

We will also create a gridded reference evapotranspiration map using data from automatic weather stations. The gridded reference evapotranspiration and fraction of vegetation cover will be used to estimate actual evapotranspiration on a regional Size: 3MB. requirements. A basic multi-temporal approach is the use of leaf-on leaf-off images, which provides greater vegetation phenology information that is available with single images [7].

Seasonal images have also been used in land cover classification with some success [7] [8] [9].File Size: 5MB. IMPACT OF TOPOGRAPHIC CORRECTION ON SOIL AND VEGETATION COVER SPECTRAL CHARACTERIZATION BY TM/LANDSAT 5 IMAGERY ABSTRACT: Several topographic correction methods have been developed to be applied to orbital imagery.

The main objective of these initiatives has been focused on improving land cover by: 1. All three images in your project use band combinations that emphasize vegetation, making the boundaries between the lake and the surrounding landscape more clear and distinct.

Next, you'll compare the imagery to the later imagery to see how the lake has changed. interactive capabilities of ERDAS, total 50 observations in the area, were selected for land use/ land cover classes at the Level II. The digital interpretation was modified and corrected in accordance with the conditions of the area.

By re-coding the land use/land cover classes, three maps, such as, land use/land cover, majorAuthor: Harendra S. Teotia.

Abstract. Usefulness of Landsat imagery in discerning major arid zone soils has been studied. Results are based on analysis of Band 7 coverage and Band 5 and 7 for a limited area followed by a comparison of these with the known soil distribution as seen in Bikaner, Jodhpur and part of Jalore, Pali and Nagaur by: 2.

of future land use change under various policy scenarios [10]. 4 Conclusion We have provided an overview of a range of land cover and land use change products developed using multitemporal Landsat image data.

The advent of widely available and less expensive Landsat-7 ETM+ has permitted the development of highly accurate land cover map by: A classification map of this area with four hydrologically important classes (agricultural vegetation, forest, wetland, and bare ground) was generated from the Landsat data.

Since the soils data necessary for curve numbers were available for only about half the watershed, the missing soils data were interpolated from the vegetation patterns of. Landsat Vegetation Indices Vegetation Indices (VIs) are combinations of DNs or surface reflectances at two or more wavelengths designed to highlight a particular property of vegetation.

Analyzing vegetation using remotely sensed data requires knowledge of the structure and function of vegetation and its reflectance properties.

soils and tropical soils have suggested that the use of satellite images can assist in the discrimination of surface information. Because a spectral response is also an individual trait, the use of this technique allows for the separation of soil classes and may therefore aid in pedological surveys.

Because soil properties are. Landsat remotely sensed spectral data can serve as useful environmental covari-ates for digitally mapping soil distribution on the landscape. This is particularly true in arid and semiarid areas where there is a range in vegetation cover and mineralog-ical properties of the soil surface and/or parent material are not completely covered by File Size: 2MB.

Urban expansion and unprecedented rural to urban transition, along with a huge population growth, are major driving forces altering land cover/use in metropolitan areas.

Many of the land cover classes such as farmlands, wetlands, forests, and bare soils have been transformed during the past years into human settlements. Identification of the city growth Cited by:. Estimating vegetation cover in an urban environment based on Landsat ETM+ imagery: A case study in Phoenix, USA In remote sensing of ecosystem properties there is a real need for regression methods that treat variables symmetrically and make no assumption about relative.within the appropriate land-cover study presentation.

Landsat Large-Area Estimates for Land Cover ber of sampled segments in the non-Landsat area in land use stratum s. 2) The second stratum category corresponds to the e be the unbiased direct expansion estimate for the acres of crop c where Yjse is the acres reported to crop c, in.images.

This index is based on the difference between strong reflection of TIR radiation and near total absorption of middle infrared (MIR) wavelengths by ba re-soil (Chen, et al., ).

It is effective in distinguishing bare-soil from similarly built-up and vegetation. Sensors on Landsat satellites have been collecting images of the.