Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods

An unprecedented spectroscopic data stream will soon become available with forthcoming Earth-observing satellite missions equipped with imaging spectroradiometers. This data stream will open up a vast array of opportunities to quantify a diversity of biochemical and structural vegetation properties. The processing requirements for such large data streams require reliable retrieval techniques enabling the spatiotemporally explicit quantification of biophysical variables. With the aim of preparing for this new era of Earth observation, this review summarizes the state-of-the-art retrieval methods that have been applied in experimental imaging spectroscopy studies inferring all kinds of vegetation biophysical variables. Identified retrieval methods are categorized into: (1) parametric regression, including vegetation indices, shape indices and spectral transformations; (2) nonparametric regression, including linear and nonlinear machine learning regression algorithms; (3) physically based, including inversion of radiative transfer models (RTMs) using numerical optimization and look-up table approaches; and (4) hybrid regression methods, which combine RTM simulations with machine learning regression methods. For each of these categories, an overview of widely applied methods with application to mapping vegetation properties is given. In view of processing imaging spectroscopy data, a critical aspect involves the challenge of dealing with spectral multicollinearity. The ability to provide robust estimates, retrieval uncertainties and acceptable retrieval processing speed are other important aspects in view of operational processing. Recommendations towards new-generation spectroscopy-based processing chains for operational production of biophysical variables are given

Investigation the soil aquifer treatment for domestic wastewater treatment, Xaysetha District Vientiane capital, Lao PDR

The study is being carried out to investigate the potential for applying SAT in Xaysetha district, Lao PDR and investigation the most suitable site for SAT in Xaysetha district. The methodology was used MCDA, GIS, RRA and semistructured interview to rank SAT site and investigate the physical, social and economic factor at the most suitable site (Nonvay site). The results of SAT ranking indicated that Xaysetha district has a potential to construct up to 3 high suitable site, 8 moderate suitable sites, and 6 low suitable sites. On the other hand, the results of physical, social and economic assessment at Nonvay site represented that DO was exceeded the Lao National Environmental Standard, and the soil infiltration rate is about 24 mm/hour (0.58 m/day). The households around Nonvay site have their own land and they access to water use and have a relationship with 9 organizations. They product wastewater was estimate 150 liter/person/day. And the land available for SAT is worth to US$ 39 million