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 Spatial Modeling of Soil Salinity Using Remote Sensing, GIS, and Field Data
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Spatial Modeling of Soil Salinity Using Remote Sensing, GIS, and Field Data

издательство Книга по Требованию Цифровая связь. Теоретические основы и практическое применение. Обзор технологий и отрасли.
Cтраниц 112        мягкая обложка       
Аннотация:Five images were acquired from Aster, Ikonos, and Landsat to check the correlation between measured soil salinity and remote sensing data. This study has demonstrated a more efficient and accurate way of estimating soil salinity from remote sensing data that should help the efforts toward sustainable agriculture. Any integration of field data, GIS, and remote sensing is considered weak unless some strong statistical measures are introduced. This study has shown the benefit of using satellite images in generating accurate soil salinity maps. Remote sensing data has great potential for monitoring dynamic processes, including salinization. In its early stages, soil salinization reduces soil productivity, but in advanced stages, salinization kills all vegetation and transforms fertile and productive land to barren waste. The environmental damage that salinity can cause is massive. Observed data was used in conjunction with satellite images. Corn and alfalfa crops were selected as indicators of soil salinity. Three models were applied to predict soil salinity from remote sensing: the ordinary least squares model (OLS), spatial autoregressive model (SAR), and modified kriging model.
Издательство: Книга по Требованию
Год:2008
Цена:
2007 руб
Нет в наличии
Поиск: Spatial Modeling of Soil Salinity Using Remote Sensing, GIS, and Field Data
НОВЫЙ ГОД СИМВОЛ 2013 ГОДАУКРАШЕНИЯ
КАРНАВАЛ НА НОВЫЙ ГОД РАСПРОДАЖА
Ортомолекулярная медицина Orthomol Устрание причин заболеваний. Ортомоль это новый метод для лечения и профилактики болезней. Orthomol - ортомолекулярная медицина.
Evaluating the GPRS Radio Interface for Different Quality of Service...

I co ras o he models developed i , our approach addi io ally represe s he mobili y of users hrough arrival ra es of ew GSM a d GPRS users as well as ha dover ra es of GSM a d GPRS users from eighbori g cells. Fur hermore, we co sider users wi h differe QoS profiles modeled by a weigh ed fair queuei g scheme accordi g o . he remai der of he paper is orga ized as follows. Sec io 2 describes he basic GPRS e work archi ec ure, he radio i erface, a d differe QoS profiles, which will be co sidered i he simula or. I Sec io 3 we describe he sof ware archi ec ure of he GPRS simula or, de ails abou he mobili y of GSM a d GPRS users, he way we modeled quali y of service profiles, a d he workload model we used. Resul s of he simula io s udies are prese ed i Sec io 4. We provide curves for average carried raffic a d packe loss probabili ies for differe cha el alloca io schemes a d packe priori ies as well as curves for average hroughpu per GPRS user.3 he Simula io Model We co sider a clus er comprisi g of sever hexadiago al cells i a i egra ed GSM/GPRS e work, servi g circui -swi ched voice a d packe -swi ched da a calls. he performa ce s udies prese ed i Sec io 4 were co duc ed for he i ermos cell of he seve cell clus er.
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