974 resultados para Remote Sensing and LiDAR Data Water Quality


Relevância:

100.00% 100.00%

Publicador:

Resumo:

This monthly report from the Iowa Department of Natural Resources is about the water quality management of Iowa's rivers, streams and lakes.

Relevância:

100.00% 100.00%

Publicador:

Resumo:

Special Points of Interest: • The Division of Soil Conservation celebrated its 70th anniversary July 1, 2009. The Iowa Soil Conservation: Laws were enacted in 1939 creating the state soil conservation agency and governing committee and providing for the creation of Iowa’s 100 soil and water conservation districts. • The Mines & Minerals Bureau, through the federal Abandoned Mine Land (AML) Program, worked with various watershed groups to again secure an additional $1 million dollars in funding for the construction on projects in Marion, Mahaska and Monroe Counties. • Iowa hosted the Mississippi River/Gulf of Mexico Hypoxia Task Force tour and meeting in September 2009.

Relevância:

100.00% 100.00%

Publicador:

Resumo:

Among the types of remote sensing acquisitions, optical images are certainly one of the most widely relied upon data sources for Earth observation. They provide detailed measurements of the electromagnetic radiation reflected or emitted by each pixel in the scene. Through a process termed supervised land-cover classification, this allows to automatically yet accurately distinguish objects at the surface of our planet. In this respect, when producing a land-cover map of the surveyed area, the availability of training examples representative of each thematic class is crucial for the success of the classification procedure. However, in real applications, due to several constraints on the sample collection process, labeled pixels are usually scarce. When analyzing an image for which those key samples are unavailable, a viable solution consists in resorting to the ground truth data of other previously acquired images. This option is attractive but several factors such as atmospheric, ground and acquisition conditions can cause radiometric differences between the images, hindering therefore the transfer of knowledge from one image to another. The goal of this Thesis is to supply remote sensing image analysts with suitable processing techniques to ensure a robust portability of the classification models across different images. The ultimate purpose is to map the land-cover classes over large spatial and temporal extents with minimal ground information. To overcome, or simply quantify, the observed shifts in the statistical distribution of the spectra of the materials, we study four approaches issued from the field of machine learning. First, we propose a strategy to intelligently sample the image of interest to collect the labels only in correspondence of the most useful pixels. This iterative routine is based on a constant evaluation of the pertinence to the new image of the initial training data actually belonging to a different image. Second, an approach to reduce the radiometric differences among the images by projecting the respective pixels in a common new data space is presented. We analyze a kernel-based feature extraction framework suited for such problems, showing that, after this relative normalization, the cross-image generalization abilities of a classifier are highly increased. Third, we test a new data-driven measure of distance between probability distributions to assess the distortions caused by differences in the acquisition geometry affecting series of multi-angle images. Also, we gauge the portability of classification models through the sequences. In both exercises, the efficacy of classic physically- and statistically-based normalization methods is discussed. Finally, we explore a new family of approaches based on sparse representations of the samples to reciprocally convert the data space of two images. The projection function bridging the images allows a synthesis of new pixels with more similar characteristics ultimately facilitating the land-cover mapping across images.

Relevância:

100.00% 100.00%

Publicador:

Resumo:

Selostus: Maatalous, fosfori ja veden laatu: alkuperä, kulkeutuminen ja vesistökuormituksen hallinta

Relevância:

100.00% 100.00%

Publicador:

Resumo:

One way of classifying water quality is by means of indices, in which a series of parameters analyzed are joined a single value, facilitating the interpretation of extensive lists of variables or indicators, underlying the classification of water quality. The objective of this study was to develop a statistically based index to classify water according to the Irrigation Water Quality Index (IWQI), to evaluate the ionic composition of water for use in irrigation and classify it by its source. For this purpose, the database generated during the Technology Generation and Adaptation (GAT) program was used, in which, as of 1988, water samples were collected monthly from water sources in the states of Paraíba, Rio Grande do Norte and Ceará. To evaluate water quality, the electrical conductivity (EC) of irrigation water was taken as a reference, with values corresponding to 0.7 dS m-1. The chemical variables used in this study were: pH, EC, Ca, Mg, Na, K, Cl, HCO3, CO3, and SO4. The data of all characteristics evaluated were standardized and data normality was confirmed by Lilliefors test. Then the irrigation water quality index was determined by an equation that relates the standardized value of the variable with the number of characteristics evaluated. Thus, the IWQI was classified based on indices, considering normal distribution. Finally, these indices were subjected to regression analysis. The method proposed for the IWQI allowed a satisfactory classification of the irrigation water quality, being able to estimate it as a function of EC for the three water sources. Variation in the ionic composition was observed among the three sources and within a single source. Although the water quality differed, it was good in most cases, with the classification IWQI II.

Relevância:

100.00% 100.00%

Publicador:

Resumo:

Selostus: Maatalousekosysteemien analysointi ja sadon ennustaminen kaukokartoituksen avulla

Relevância:

100.00% 100.00%

Publicador:

Resumo:

Special Points of Interest: • The Division of Soil Conservation celebrated its 70th anniversary July 1, 2009. The Iowa Soil Conservation Laws were enacted in 1939 creating the state soil conservation agency and governing committee and providing for the creation of Iowa’s 100 soil and water conservation districts. • The Mines & Minerals Bureau, through the federal Abandoned Mine Land (AML) Program, worked with various watershed groups to again secure an additional $1 million dollars in funding for the construction on projects in Marion, Mahaska and Monroe Counties. • Iowa hosted the Mississippi River/Gulf of Mexico Hypoxia Task Force tour and meeting in September 2009.

Relevância:

100.00% 100.00%

Publicador:

Resumo:

Remote sensing image processing is nowadays a mature research area. The techniques developed in the field allow many real-life applications with great societal value. For instance, urban monitoring, fire detection or flood prediction can have a great impact on economical and environmental issues. To attain such objectives, the remote sensing community has turned into a multidisciplinary field of science that embraces physics, signal theory, computer science, electronics, and communications. From a machine learning and signal/image processing point of view, all the applications are tackled under specific formalisms, such as classification and clustering, regression and function approximation, image coding, restoration and enhancement, source unmixing, data fusion or feature selection and extraction. This paper serves as a survey of methods and applications, and reviews the last methodological advances in remote sensing image processing.

Relevância:

100.00% 100.00%

Publicador:

Resumo:

Summary of water quality data collected from Iowa streams from 2000 through 2004.

Relevância:

100.00% 100.00%

Publicador:

Resumo:

Summary of water quality data collected from streams in 2008

Relevância:

100.00% 100.00%

Publicador:

Resumo:

Water quality summary of stream data collected from 2000 through 2008.

Relevância:

100.00% 100.00%

Publicador:

Resumo:

Summary of stream water quality data collected in 2009.

Relevância:

100.00% 100.00%

Publicador:

Resumo:

Summary of stream water quality data collected from 2000 through 2009.

Relevância:

100.00% 100.00%

Publicador:

Resumo:

Summary of water quality data from stream in Iowa from 2000 through 2010.

Relevância:

100.00% 100.00%

Publicador:

Resumo:

Summary of water quality data collected from stream in 2010.